Target coverage in camera networks for manufacturing workplaces

In this paper, we investigate the camera network placement problem for target coverage in manufacturing workplaces. The problem is formulated to find the minimum number of cameras of different types and their best configurations to maximise the coverage of the monitored workplace such that the given set of target points of interest are each k-covered with a predefined minimum spatial resolution. Since the problem is NP-complete, and even NP-hard to approximate, a novel method based on Simulated Annealing is presented to solve the optimisation problem. A new neighbourhood generation function is proposed to handle the discrete nature of the problem. The visual coverage is modelled using realistic and coherent assumptions of camera intrinsic and extrinsic parameters making it suitable for many real world camera based applications. Task-specific quality of coverage measure is proposed to assist selecting the best among the set of camera network placements with equal coverage. A 3D CAD of the monitored space is used to examine physical occlusions of target points. The results show the accuracy, efficiency and scalability of the presented solution method; which can be applied effectively in the design of practical camera networks.     

An investigation of the different TLM configurations used in the modelling of diffusion problems

The commonly used transmission-line modelling (TLM) network of one-dimensional diffusion problems often results in unwanted oscillations. Two different TLM configurations (line boundary (LB) and resistance boundary (RB)), are investigated with some techniques necessary to reduce the oscillations. Comparisons between the various LB and RB models addressed in this paper, indicate that, in general, two-dimensional TLM models produce consistently more accurate results.     

Chlorination of model drinking water biofilm: implications for growth and organic carbon removal

The influence of chlorine on biofilm in low organic carbon environments typical of drinking water or industrial process water was examined by comparing biomass and kinetic parameters for biofilm growth in a chlorinated reactor to those in a non-chlorinated control. Mixed-population heterotrophic biofilms were developed in rotating annular reactors under low concentration, carbon-limited conditions (< 2 mg/L as carbon) using three substrate groups (amino acids, carbohydrates and humic substances). Reactors were operated in parallel under identical conditions with the exception that chlorine was added to one reactor at a dose sufficient to maintain a free chlorine residual of 0.09-0.15 mg/L in the effluent. The presence of free chlorine resulted in development of less biofilm biomass compared to the control for all substrates investigated. However, specific growth and organic carbon removal rates were on the average five times greater for chlorinated biofilm compared to the control. Observed yield values were less for chlorinated biofilm. Although chlorinated biofilm's specific organic carbon removal rate was high, the low observed yield indicated organic carbon was being utilized for purposes other than creating new cell biomass. The impacts of free chlorine on mixed-population biofilms in low-nutrient environments were different depending upon the available substrate. Biofilms grown using amino acids exhibited the least difference between control and chlorinated kinetic parameters; biofilm grown using carbohydrates had the greatest differences. These findings are particularly relevant to the fundamental kinetic parameters used in models of biofilm growth in piping systems that distribute chlorinated, low-carbon-concentration water.     

A systematic literature review on Internet of things in education: Benefits and challenges

With close to 20.4 billion devices connected to the Internet to be deployed by 2020, Internet of things (IoT) is already being leveraged in diverse sectors. Now, because of the ubiquitous nature of IoT devices, schools and academic institutions are looking to incorporate IoT in educational activities. With the increased use of IoT in the education domain, it is of utmost importance to study how this technology with its distinguished system functions such as sensing and decision making can support and challenge the pedagogical processes for all interrelated actors (faculty, students, and staff) as well as all involved assets (e.g., libraries, classrooms, and labs). Although there have been several contributions on the inclusion of IoT into the education domain, there is still a lack of consolidated and coherent views on this subject. Hence, we are motivated to close the gap of knowledge and embarked on mapping out the published studies available. This study presents the results of a systematic literature review focusing on the benefits and the challenges faced in education in integrating IoT into the curriculum and educational environments. Different mapping views of the extracted studies are provided as long as a summary of the already implemented tools and a list of gap research questions yet to be investigated.     

Advances in machine learning applications for scenario intelligence: deep learning

In this paper, we discuss the inextricable link between automating training environment adaptation and deep understanding of the context surrounding specific decisions and actions executed in the performance environment. To enable deep contextual understanding, psychological measurement strategies are needed to more accurately and rapidly model the psychologically meaningful details of the trainee's interactions with events, objects, and people in the training environment. As these interactions often entail complex, nonlinear cue-action relationships, the underlying models must effectively capture the nuance, complexity, and largely intuitive nature of human decision-making. This paper discusses the promise of an emerging field of machine learning – deep neural networks – for supporting this requirement.     

Cycle time-oriented mid-term production planning for semiconductor wafer fabrication

Wafers are produced in an environment with uncertain demand and failure-prone machines. Production planners have to react to changes of both machine availability and target output, and revise plans appropriately. The scientific community mostly proposes WIP-oriented mid-term production planning to solve this problem. In such approaches, production is planned by defining targets for throughput rates and buffer levels of selected operations. In industrial practice, however, cycle time-oriented planning is often preferred over WIP-oriented planning. We therefore propose a new linear programming formulation, which facilitates cycle time-oriented mid-term production planning in wafer fabrication. This approach plans production by defining release quantities and target cycle times up to selected operations. It allows a seamless integration with the subordinate scheduling level. Here, least slack first scheduling translates target cycle times into lot priorities. We evaluate our new methodology in a comprehensive simulation study. The results suggest that cycle time-oriented mid-term production planning can both increase service level and reduce cycle time compared to WIP-oriented planning. Further, it requires less modelling effort and generates plans, which are easier to comprehend by human planners.     

Industrial model predictive control technology as applied to continuous polymerization processes

This paper will review the progress to date concerning the application of the new Model Predictive Control (MPC) tools to a prototype continuous polymerization (CP) process. It will discuss in general why the MPC technology maybe a good fit for the CP control problems (minimize settling time after transition, multivariable interactions, unusual process dynamics). It will also discuss our experience with these tools.     

The kinetics of ettringite formation

Conversion of hemihydrate to gypsum, and ettringite formation in the system C₃A---CaSO₄·1/2H₂O---H₂O three hydration temperatures using x-ray diffraction. Analysis of the kinetics of gypsum formation suggests that the rate limiting step changes from an initial dependence on the hemihydrate surface area to a dependence on the gypsum growth rate. The formation of ettringite was found to be controlled by diffusion. In accord with the needle-like morphology that ettringite exhibits, a nucleation and growth model predicted one-dimensional growth. An apparent activation energy calculated for ettringite formation is consistent with a diffusionally controlled process.     

Pavlov's Bugs: Matching Repair Policies with Rewards

Software maintenance engineers devote a significant amount of work to repairing user-identified errors. But user, maintainer, and manager perceptions of an error's importance can vary, and bug-repair assignment policies can adversely affect those perceptions.