Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Improving the economic value of photographic screening for optical coherence tomography-detectable macular oedema : a prospective, multicentre, UK study

Peer reviewedPublisher PD

Prostate cancer prognostication based on clinical and histopathological tumor features

Prostate cancer is the second most common cancer in men worldwide. Of almost 1.3 million newly diagnosed men per year, up to 80% will have localized disease with a characteristically prolonged natural history. Risk stratification and treatment decision-making for these men is currently based on the combination of standard clinical and histopathological predictors, such as the Gleason score, prostate specific antigen (PSA) level and clinical tumor stage at diagnosis. However, these standard predictors are not sufficient to capture the heterogeneity in prognosis for men with localized prostate cancer. As a consequence, these men are often overtreated and may suffer from treatment-related side effects. In this thesis we aimed to improve prognostication for men with localized prostate cancer through validation of existing risk stratification tools based on standard clinical and histopathological factors, and through validation of existing, and identification of novel, prognostic markers. In Study I, we evaluated if the nested case-control study design is appropriate for estimating relative and absolute risks of dying from prostate cancer in the presence of competing risks. We used a case-control study (ProMort I) nested in the National Prostate Cancer Register of Sweden (NPCR). We found that the relative risks of dying from prostate cancer estimated in ProMort I were comparable to the relative risks estimated in the NPCR. The relative risks of dying from other causes estimated in ProMort I were biased, which led to biased estimates of the absolute risks of dying from prostate cancer. The bias in both the relative and absolute risks was reduced by augmenting competing-risks cases, and especially by augmenting both the competing-risks cases and the controls. Our results indicate that, without the additional extensions to the design, the nested-case control studies are not suitable for the development of models predicting death from prostate cancer in the presence of competing risks. In Study II, we systematically compared the prognostic performance of the most commonly used pretreatment risk stratification tools in predicting death from prostate cancer using data from the Prostate Cancer data Base of Sweden. The Memorial Sloan Kettering Cancer Center nomogram, Cancer of the Prostate Risk Assessment score and Cambridge Prognostic Groups discriminated death from prostate cancer better than the D’Amico and D’Amico-derived risk grouping systems. The order of performance remained after stratifying by primary treatment and year of diagnosis. Using these tools could improve clinical decision-making. In Study III, we evaluated if a virtual microscopy system which we developed for central rereview in ProMort I and Study IV can be used interchangeably with standard light microscopy for the histopathological evaluation of prostate cancer. We found good repeatability (i.e., intra-observer agreement) and reproducibility (i.e., inter-observer agreement) for several key prostate cancer histopathological features (i.e., core length, tumor length, primary and secondary Gleason pattern, the Gleason score and the Gleason Grade Groups (GGs)) both within and between light and virtual microscopy. The repeatability and/or reproducibility for some of the rare, or less commonly reported, features and for the percentage of Gleason pattern 4 was poor. The repeatability and/or reproducibility for these features should be improved before they are used in prognostic models. For all evaluated features, the agreement was similar within and between light and virtual microscopy indicating that light microscopy and our internally developed virtual microscopy system can be used interchangeably for the histopathological evaluation of prostate cancer. In Study IV, we evaluated if the International Society of Urological Pathology (ISUP) revisions of the Gleason grading systems have improved prostate cancer prognostication. We used a nested case-control study (ProMort II) to compare the prognostic performance of the pre-2005 Gleason score and the ISUP 2014 Gleason score. In our study, the ISUP 2014 Gleason score discriminated death from prostate cancer better than the pre-2005 Gleason score. Our results also indicate that this improvement may be due to classifying all cribriform patterns, rather than poorly formed glands, as Gleason pattern 4. We then evaluated if other histopathological features can further improve the prediction of death from prostate cancer. The number of cores with ≥50% cancer involvement, comedonecrosis and high-grade prostatic intraepithelial neoplasia (HGPIN) predicted death from prostate cancer independently of the GGs. Only comedonecrosis and HGPIN remained independent predictors when added to the model with all the standard predictors (the GGs, age, PSA and clinical tumor stage at diagnosis). Adding these features had minimal impact on the model discrimination

The grading inspection of an agricultural product: decision-making problems and strategies with their training and selection implications

This research thesis describes an investigation into the grading inspection of apples with particular reference to the decision-making component of the inspection task. The research commences with an evaluation, conducted across seven grading packhouses in the United Kingdom, of the correctness and consistency with which examiners judge and classify fruit in accordance with the European Economic Community Standards and attempts to broadly answer two questions: (i) How well do human inspectors of apples perform under optimum conditions (the decision task with trivial search)? and (ii) how well do human inspectors of apples perform under actual 'on-line' conditions (the decision task with active search)? Subsequent analysis identifies those factors contributing to poor decision-making performance, of which four are the subject of further investigation. These are inspector training, selection of inspectors, the deployment of inspectors, and the method of presentation of fruit. [Continues.

Volume 2 – Conference

We are pleased to present the conference proceedings for the 12th edition of the International Fluid Power Conference (IFK). The IFK is one of the world’s most significant scientific conferences on fluid power control technology and systems. It offers a common platform for the presentation and discussion of trends and innovations to manufacturers, users and scientists. The Chair of Fluid-Mechatronic Systems at the TU Dresden is organizing and hosting the IFK for the sixth time. Supporting hosts are the Fluid Power Association of the German Engineering Federation (VDMA), Dresdner Verein zur Förderung der Fluidtechnik e. V. (DVF) and GWT-TUD GmbH. The organization and the conference location alternates every two years between the Chair of Fluid-Mechatronic Systems in Dresden and the Institute for Fluid Power Drives and Systems in Aachen. The symposium on the first day is dedicated to presentations focused on methodology and fundamental research. The two following conference days offer a wide variety of application and technology orientated papers about the latest state of the art in fluid power. It is this combination that makes the IFK a unique and excellent forum for the exchange of academic research and industrial application experience. A simultaneously ongoing exhibition offers the possibility to get product information and to have individual talks with manufacturers. The theme of the 12th IFK is “Fluid Power – Future Technology”, covering topics that enable the development of 5G-ready, cost-efficient and demand-driven structures, as well as individual decentralized drives. Another topic is the real-time data exchange that allows the application of numerous predictive maintenance strategies, which will significantly increase the availability of fluid power systems and their elements and ensure their improved lifetime performance. We create an atmosphere for casual exchange by offering a vast frame and cultural program. This includes a get-together, a conference banquet, laboratory festivities and some physical activities such as jogging in Dresden’s old town.:Group 1 | 2: Digital systems Group 3: Novel displacement machines Group 4: Industrial applications Group 5: Components Group 6: Predictive maintenance Group 7: Electro-hydraulic actuatorsDer Download des Gesamtbandes wird erst nach der Konferenz ab 15. Oktober 2020 möglich sein.:Group 1 | 2: Digital systems Group 3: Novel displacement machines Group 4: Industrial applications Group 5: Components Group 6: Predictive maintenance Group 7: Electro-hydraulic actuator

A Case Study Based Approach for Remote Fault Detection Using Multi-Level Machine Learning in A Smart Building

Due to the increased awareness of issues ranging from green initiatives, sustainability, and occupant well-being, buildings are becoming smarter, but with smart requirements come increasing complexity and monitoring, ultimately carried out by humans. Building heating ventilation and air-conditioning (HVAC) units are one of the major units that consume large percentages of a building’s energy, for example through their involvement in space heating and cooling, the greatest energy consumption in buildings. By monitoring such components effectively, the entire energy demand in buildings can be substantially decreased. Due to the complex nature of building management systems (BMS), many simultaneous anomalous behaviour warnings are not manageable in a timely manner; thus, many energy related problems are left unmanaged, which causes unnecessary energy wastage and deteriorates equipment’s lifespan. This study proposes a machine learning based multi-level automatic fault detection system (MLe-AFD) focusing on remote HVAC fan coil unit (FCU) behaviour analysis. The proposed method employs sequential two-stage clustering to identify the abnormal behaviour of FCU. The model’s performance is validated by implementing well-known statistical measures and further cross-validated via expert building engineering knowledge. The method was experimented on a commercial building based in central London, U.K., as a case study and allows remotely identifying three types of FCU faults appropriately and informing building management staff proactively when they occur; this way, the energy expenditure can be further optimized

Lost Revenue Recognition in E-commerce: Identifying Causes and Implications

openIn order for e-commerce companies to remain financially healthy, revenue recognition is essential. The sector encounters various problems, including slow-loading pages, problematic web resources, and errors, which can result in revenue losses. This research aims to investigate the causes of lost revenue in e-commerce and propose algorithmic solutions to address these challenges. This study examines the impact of slow pages, problematic web resources, and errors on revenue recognition in the e-commerce industry. In this study, real-world data is analyzed and used to identify how these factors negatively impact e-commerce companies' revenue. Additionally, the study proposes a methodology for calculating the potential revenue that could have been recognized in the absence of the identified factors contributing to lost revenue recognition. This methodology incorporates machine learning techniques, and data visualization tools to estimate the extent of revenue loss. The resulting insights provide decision-makers in e-commerce companies with valuable information for optimizing revenue recognition strategies and maximizing financial performance. This research aims to identify the causes of revenue loss in e-commerce. In order to improve revenue recognition accuracy and maximize business performance in the digital marketplace, e-commerce companies will need to develop algorithmic solutions and implement revenue calculation methodologies to address these issues

Models predicting survival to guide treatment decision-making in newly diagnosed primary non-metastatic prostate cancer: a systematic review.

OBJECTIVES: Men diagnosed with non-metastatic prostate cancer require standardised and robust long-term prognostic information to help them decide on management. Most currently-used tools use short-term and surrogate outcomes. We explored the evidence base in the literature on available pre-treatment, prognostic models built around long-term survival and assess the accuracy, generalisability and clinical availability of these models. DESIGN: Systematic literature review, pre-specified and registered on PROSPERO (CRD42018086394). DATA SOURCES: MEDLINE, Embase and The Cochrane Library were searched from January 2000 through February 2018, using previously-tested search terms. ELIGIBILITY CRITERIA: Inclusion required a multivariable model prognostic model for non-metastatic prostate cancer, using long-term survival data (defined as ≥5 years), which was not treatment-specific and usable at the point of diagnosis. DATA EXTRACTION AND SYNTHESIS: Title, abstract and full-text screening were sequentially performed by three reviewers. Data extraction was performed for items in the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Individual studies were assessed using the new Prediction model Risk Of Bias ASsessment Tool. RESULTS: Database searches yielded 6581 studies after deduplication. Twelve studies were included in the final review. Nine were model development studies using data from over 231 888 men. However, only six of the nine studies included any conservatively managed cases and only three of the nine included treatment as a predictor variable. Every included study had at least one parameter for which there was high risk of bias, with failure to report accuracy, and inadequate reporting of missing data common failings. Three external validation studies were included, reporting two available models: The University of California San Francisco (UCSF) Cancer of the Prostate Risk Assessment score and the Cambridge Prognostic Groups. Neither included treatment effect, and both had potential flaws in design, but represent the most robust and usable prognostic models currently available. CONCLUSION: Few long-term prognostic models exist to inform decision-making at diagnosis of non-metastatic prostate cancer. Improved models are required to inform management and avoid undertreatment and overtreatment of non-metastatic prostate cancer.The Urology Foundation - Research Scholarship

The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology