Surrogate-based optimization of a periodic rescheduling algorithm

Periodic rescheduling is an iterative method for real-time decision-making on industrial process operations. The design of such methods involves high-level when-to-schedule and how-to-schedule decisions, the optimal choices of which depend on the operating environment. The evaluation of the choices typically requires computationally costly simulation of the process, which—if not sufficiently efficient—may result in a failure to deploy the system in practice. We propose the continuous control parameter choices, such as the re-optimization frequency and horizon length, to be determined using surrogate-based optimization. We demonstrate the method on real-time rebalancing of a bike sharing system. Our results on three test cases indicate that the method is useful in reducing the computational cost of optimizing an online algorithm in comparison to the full factorial sampling.     

Design Methodology for Offloading Software Executions to FPGA

Field programmable gate array (FPGA) is a flexible solution for offloading part of the computations from a processor. In particular, it can be used to accelerate an execution of a computationally heavy part of the software application, e.g., in DSP, where small kernels are repeated often. Since an application code for a processor is a software, a design methodology is needed to convert the code into a hardware implementation, applicable to the FPGA. In this paper, we propose a design method, which uses the Transport Triggered Architecture (TTA) processor template and the TTA-based Co-design Environment toolset to automate the design process. With software as a starting point, we generate a RTL implementation of an application-specific TTA processor together with the hardware/software interfaces required to offload computations from the system main processor. To exemplify how the integration of the customized TTA with a new platform could look like, we describe a process of developing required interfaces from a scratch. Finally, we present how to take advantage of the scalability of the TTA processor to target platform and application-specific requirements.     

Low-Power Application-Specific Processor for FFT Computations

In this paper, a processor architecture tailored for radix-4 and mixed-radix FFT computations is described. The processor has native support for power-of-two transform sizes. Several optimizations have been used to improve the energy-efficiency of the processor and experiments show that a programmable solution can possess energy-efficiency comparable to fixed-function ASICs.     

Enhancing personal communication with spatial haptics: Two scenario-based experiments on gestural interaction

Haptic gestures and sensations through the sense of touch are currently unavailable in remote communication. There are two main reasons for this: good quality haptic technology has not been widely available and knowledge on the use of this technology is limited. To address these challenges, we studied how users would like to, and managed to create spatial haptic information by gesturing. Two separate scenario-based experiments were carried out: an observation study without technological limitations, and a study on gesturing with a functional prototype with haptic actuators. The first study found three different use strategies for the device. The most common gestures were shaking, smoothing and tapping. Multimodality was requested to create the context for the communication and to aid the interpretation of haptic stimuli. The second study showed that users were able to utilize spatiality in haptic messages (e.g., forward–backward gesture for agreement). However, challenges remain in presenting more complex information via remote haptic communication. The results give guidance for communication activities that are usable in spatial haptic communication, and how to make it possible to enable this form of communication in reality.     

A Test of Two Methods for Waste Rock Drainage Quality Prediction: Aqua Regia Extraction and Single-addition Net-acid Generation Test Leachate Analysis

Mine Water and the Environment - The mobility of contaminants from mine waste can be assessed using different extraction methods. Aqua regia (AR) extraction is the most commonly used method in...     

Personalising speech-to-speech translation: Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis

In this paper we present results of unsupervised cross-lingual speaker adaptation applied to text-to-speech synthesis. The application of our research is the personalisation of speech-to-speech translation in which we employ a HMM statistical framework for both speech recognition and synthesis. This framework provides a logical mechanism to adapt synthesised speech output to the voice of the user by way of speech recognition. In this work we present results of several different unsupervised and cross-lingual adaptation approaches as well as an end-to-end speaker adaptive speech-to-speech translation system. Our experiments show that we can successfully apply speaker adaptation in both unsupervised and cross-lingual scenarios and our proposed algorithms seem to generalise well for several language pairs. We also discuss important future directions including the need for better evaluation metrics.     

An 8-hour system for Salmonella detection with immunomagnetic separation and homogeneous time-resolved fluorescence PCR

We describe a system consisting of rapid sample enrichment and homogeneous end-point PCR analysis that enables the detection of Salmonella in various food matrices in 8 h. Sample preparation starts with 6 h enrichment step in supplemented broth, after which Salmonella cells are collected with immunomagnetic particles. The particles are washed and dispensed to ready-to-use PCR reaction vessels, which contain dried assay-specific reagents and an internal amplification control. PCR is performed with a novel instrument platform utilising the sensitive label technology of time-resolved fluorometry. Qualitative assay results are automatically interpreted and available in 45 min after sample addition. The overall accuracy, sensitivity and specificity of the Magda CA Salmonella system were 99.1%, 98.4% and 100.0%, respectively, based on the evaluation of 107 samples (beef, pork, poultry and ready-to-eat meals) artificially contaminated with sub-lethally injured Salmonella cells.     

Neural networks in process life cycle profit modelling

Changes in operational environment of the process industry such as decreasing selling prices, increased competition between companies and new legislation, set requirements for performance and effectiveness of the industrial production lines and processes. For the basis of this study, a life cycle profit (LCP) model of a pulp process was constructed using different kind of process information including chemical consumptions and production levels of material and energy flows in unit processes. However, all the information needed in the creation of relevant LCP model was not directly provided by information systems of the plant. In this study, neural networks was used to model pulp bleaching process and fill out missing information and furthermore to create estimators for the alkaline chemical consumption. A data-based modelling approach was applied using an example, where factors affecting the sodium hydroxide consumption in the bleaching stage were solved. The results showed that raw process data can be refined into new valuable information using computational methods and moreover to improve the accuracy of life cycle profit models.