Monitoring of excessive deformation of steel structure Extra-High Voltage pylons

Reliability and security of a power transmission depends on the state of the power grid and mainly on the state of the Extra-High Voltage pylons. The paper deals with deformation analysis of existing steel structure of selected Extra-High Voltage pylons which showed excessive differences comparing to the original design. In the assessment of the situation, geodetic survey of selected pylons of power grid that showed the greatest deformation was performed. On taken images, deformation of steel structures by using the FOTOMNG system was also analyzed. The proposed method allows a modeling of the structure of the object based on precisely obtained photographic documentation of the current state. It also represents a very effective method which allows to quickly and efficiently analyze the deformation in the structure of Extra-High Voltage pylons in the critical position of the power grid. Other benefits include the possibility of repeatable and safe measurement.Web of Science62232932

Mineração de dados e estimativa da mortalidade alta de frangos quando expostos a onda de calor

As ondas de calor provocam perdas na produção animal pela sua exposição ao estresse por calor aumentando a mortalidade, e consequentemente, perdas econômicas. Bancos de dados zootécnicos e meteorológicos históricos podem conter informações que permitem modelar a mortalidade de frangos devido à incidência de ondas de calor. O objetivo foi analisar bancos de dados de frangos de corte associados a dados meteorológicos utilizando técnicas de mineração de dados, seleção de atributos e classificação (árvore de decisão) para modelar o impacto da incidência de onda de calor na mortalidade de frangos de corte. O Índice de Temperatura e Umidade (ITU) foi utilizado para descrever parte dos dados ambientais. A técnica de Mineração de Dados permitiu a construção de três modelos compreensíveis para estimar a alta mortalidade em frangos de corte. Os modelos gerados pela abordagem de seleção de atributos por Análise dos Componentes Principais e Wrapper apresentaram igual desempenho com uma precisão total de 89,3% e a classificação para alta mortalidade foi de 83,3%. Quando a seleção foi feita por especialistas do domínio, a precisão do modelo foi de 85,7%, e a da classificação para alta mortalidade foi de 76,9%. Resultados meteorológicos e o ITU calculada a partir de estações meteorológicas permitiram identificar condições ambientais prejudiciais para frangos entre 29 e 42 dias de vida. A técnica de Mineração de Dados é aplicável para construir modelos preditivos para a produção animal.Heat waves usually result in losses of animal production since they are exposed to thermal stress inducing an increase in mortality and consequent economical losses. Animal science and meteorological databases from the last years contain enough data in the poultry production business to allow the modeling of mortality losses due to heat wave incidence. This research analyzes a database of broiler production associated to climatic data, using data mining techniques such as attribute selection and data classification (decision tree) to model the impact of heat wave incidence on broiler mortality. The temperature and humidity index (THI) was used for screening environmental data. The data mining techniques allowed the development of three comprehensible models for estimating specifically high mortality during broiler production. Two models yielded a classification accuracy of 89.3% by using Principal Component Analysis (PCA) and Wrapper feature selection approaches. Both models obtained a class precision of 0.83 for classifying high mortality. When the feature selection was made by the domain experts, the model accuracy reached 85.7%, while the class precision of high mortality was 0.76. Meteorological data and the calculated THI from meteorological stations were helpful to select the range of harmful environmental conditions for broilers 29 and 42 days old. The data mining techniques were useful for building animal production models

PSO Based Lossless and Robust Image Watermarking using Integer Wavelet Transform

In recent days, the advances in the broadcasting of multimedia contents in digital format motivate to protect this digital multimedia content form illegal use, such as manipulation, duplication and redistribution. However, watermarking algorithms are designed to meet the requirements of different applications, because, various applications have various requirements. This paper intends to design a new watermarking algorithm with an aim of provision of a tradeoff between the robustness and imperceptibility and also to reduce the information loss. This approach applies Integer Wavelet Transform (IWT) instead of conventional floating point wavelet transforms which are having main drawback of round of error. Then the most popular artificial intelligence technique, particle swarm optimization (PSO) used for optimization of watermarking strength. The strength of watermarking technique is directly related to the watermarking constant alpha. The PSO optimizes alpha values such that, the proposed approach achieves better robustness over various attacks and an also efficient imperceptibility. Numerous experiments are conducted over the proposed approach to evaluate the performance. The obtained experimental results demonstrates that the proposed approach is superior compared to conventional approach and is able to provide efficient resistance over Gaussian noise, sal

Differential Private POI Queries via Johnson-Lindenstrauss Transform

© 2013 IEEE. The growing popularity of location-based services is giving untrusted servers relatively free reign to collect huge amounts of location information from mobile users. This information can reveal far more than just a user's locations but other sensitive information, such as the user's interests or daily routines, which raises strong privacy concerns. Differential privacy is a well-acknowledged privacy notion that has become an important standard for the preservation of privacy. Unfortunately, existing privacy preservation methods based on differential privacy protect user location privacy at the cost of utility, aspects of which have to be sacrificed to ensure that privacy is maintained. To solve this problem, we present a new privacy framework that includes a semi-trusted third party. Under our privacy framework, both the server and the third party only hold a part of the user's location information. Neither the server nor the third party knows the exact location of the user. In addition, the proposed perturbation method based on the Johnson Lindenstrauss transform satisfies the differential privacy. Two popular point of interest queries, -NN and Range, are used to evaluate the method on two real-world data sets. Extensive comparisons against two representative differential privacy-based methods show that the proposed method not only provides a strict privacy guarantee but also significantly improves performance

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform with Machine Learning Classifiers and Ensembles

Iris biometric identification allows for contactless authentication, which helps to avoid the transmission of diseases like COVID-19. Biometric systems become unstable and hazardous due to spoofing attacks involving contact lenses, replayed video, cadaver iris, synthetic Iris, and printed iris. This work demonstrates the iris presentation attacks detection (Iris-PAD) approach that uses fragmental coefficients of transform iris images as features obtained using Discrete Cosine Transform (DCT), Haar Transform, and hybrid Transform. In experimental validations of the proposed method, three main types of feature creation are investigated. The extracted features are utilized for training seven different machine learning classifiers alias Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and decision tree(J48) with ensembles of SVM+RF+NB, SVM+RF+RT, and RF+SVM+MLP (multi-layer perceptron) for proposed iris liveness detection. The proposed iris liveness detection variants are evaluated using various statistical measures: accuracy, Attack Presentation Classification Error Rate (APCER), Normal Presentation Classification Error Rate (NPCER), Average Classification Error Rate (ACER). Six standard datasets are used in the investigations. Total nine iris spoofing attacks are getting identified in the proposed method. Among all investigated variations of proposed iris-PAD methods, the 4 ×4 of fragmental coefficients of a Hybrid transformed iris image with RF algorithm have shown superior iris liveness detection with 99.95% accuracy. The proposed hybridization of transform for features extraction has demonstrated the ability to identify all nine types of iris spoofing attacks and proved it robust. The proposed method offers exceptional performances against the Synthetic iris spoofing images by using a random forest classifier. Machine learning has massive potential in a similar domain and could be explored further based on the research requirements

HighLight: Towards an Ambient Robotic Table as a Social Enabler

With smartphones becoming more commonplace in our daily lives, they often take up more time and space than we would like them to. Research shows that using smartphones during social interactions does more harm than good. With this in mind, we set out to create the first prototype of an ambient robotic table that will support social interactions and discourage digital distractions. Through a rapid prototyping process, we present HighLight, a prototype of a socially enabling robotic table that has a smartphone compartment in its center and ambient features reacting in real-time to conversations taking place around the table. We report on our contributions to the research community by investigating the design of an ambient robotic table as a social enabler that encourages social interactions through ambiance, thus exploring future directions of non-disruptive technologies that support social interactions

An Expressive Robotic Table to Enhance Social Interactions

We take initial steps into prototyping an expressive robotic table that can serve as a social mediator. The work is constructed through a rapid prototyping process consisting of five workshopbased phases with five interaction design participants. We report on the various prototyping techniques that led to the generated concept of an expressive robotic table. Our design process explores how expressive motion cues such as respiratory movements can be leveraged to mediate social interactions between people in cold outdoor environments. We conclude by discussing the implications of the different prototyping methods applied and the envisioned future directions of the work within the scope of expressive robotics

Skew Detection and Correction in Scanned Document Images.

During document scanning, skew is inevitably introduced into the incoming document image. Skew detection is one the first operations to be applied to scanned documents when converting data to a digital format. Its aim is to align an image before processing because text segmentation and recognition methods require properly aligned next lines. Different algorithms of skew detection are implemented. The first one is Scan line based skew detection. In this method the image is projected at several angles and the variance in the number of black pixels per projected scan line is determined. The angle at which the maximum variance occurs is the angle of skew.The second one is based on the Hough transform. Hough transform is performed on the scanned document image and the variance in ρ values is calculated for each value of θ. The angle that gives the maximum variance is the skew angle.The third approach is based on the base-point method. Here a concept of basepoint is introduced. After the successive base-points in every text line within a suitable sub-region were selected as samples for the straight-line fitting. The average of these baseline directions is computed, which corresponds to the degree of skew of the whole document image.All the above mentioned algorithm have been implemented and the results of each have been compared for accuracy

Three Factors to Improve Out-of-Distribution Detection

In the problem of out-of-distribution (OOD) detection, the usage of auxiliary data as outlier data for fine-tuning has demonstrated encouraging performance. However, previous methods have suffered from a trade-off between classification accuracy (ACC) and OOD detection performance (AUROC, FPR, AUPR). To improve this trade-off, we make three contributions: (i) Incorporating a self-knowledge distillation loss can enhance the accuracy of the network; (ii) Sampling semi-hard outlier data for training can improve OOD detection performance with minimal impact on accuracy; (iii) The introduction of our novel supervised contrastive learning can simultaneously improve OOD detection performance and the accuracy of the network. By incorporating all three factors, our approach enhances both accuracy and OOD detection performance by addressing the trade-off between classification and OOD detection. Our method achieves improvements over previous approaches in both performance metrics.Comment: Under revie

Investing in Skilled Specialists to Grow Hospital Infrastructure for Quality Improvement.

OBJECTIVES: Hospitals can reduce labor costs by hiring lowest skill possible for the job, stretching clinical hours, and reducing staff not at bedside. However, these labor constraints designed to reduce costs may paradoxically increase costs. Specialty staff, such as board-certified clinicians, can redesign health systems to evaluate the needs of complex patients and prevent complications. The aim of the study was to evaluate whether investing in skilled specialists for supporting hospital quality infrastructure improves value and performance. METHODS: We evaluated pressure injury rates as an indicator of performance in a retrospective observational cohort of 55 U.S. academic hospitals from the Vizient clinical database between 2007 and 2012. Pressure injuries were defined by U.S. Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator 3 (PSI-03) for stage 3, 4, and unstageable pressure injuries not present on admission in hospitalized adults. We compared ratios of board-certified wound care nurses per 1000 hospital beds to hospital-acquired pressure injury rates in these hospitals using mixed-effects regression of hospital quarters. RESULTS: High-performing hospitals invested in prevention infrastructure with skilled specialists and observed performance improvements. Regression indicated that by adding one board-certified wound care nurse per 1000 hospital beds, hospitals had associated decreases in pressure injury rates by -17.7% relative to previous quarters, controlling for other interruptions. Highest performers supplied fewer skilled specialists and achieve improved outcomes. CONCLUSIONS: Skilled specialists bring important value to health systems as a representation of investment in infrastructure, and the proportion of these specialists could be scaled relative to the hospital's patient capacity. Policy should support hospitals to make investments in infrastructure to drive down patient costs and improve quality