TE Connectivity (Germany)

The transformation of distribution networks into smart grids goes hand in hand with the implementation of state-of-the-art components to enable advanced monitoring, protection, control and communication capabilities. In order to measure the voltage on different points of a power supply system, low-power voltage transformers (LPVT) represent a promising alternative to conventional voltage transformers for various reasons and are increasingly installed in medium voltage networks dominated by underground cable lines. Besides standard operation conditions, commissioning testing of new lines, or testing for maintenance and condition monitoring purposes on existing lines has to be considered for the use of low-power voltage transformers. In order to study the potential impact of LPVTs on results of cable testing, experimental investigations using very low frequency (VLF) diagnostic measurements - representing a common practise method for medium voltage cable systems - have been performed. Results of partial discharge and dissipation factor measurements performed on different LPVT technologies are analysed and compared in this paper.

As power networks move toward increased digitalization and the smart grid is becoming more of a reality, power networks are hosting elements of smart grids to meet the growing demands and requirements of advanced digital networks. One of the components of a smart grid is the use of measuring sensors. Low-power instrument transformers are one part of these advanced systems and are increasingly being used within distribution networks. As these sensors are being widely installed, network operators must ensure that the addition of such sensors will not affect the normal operation of the network. This paper discusses the potential impact of using voltage sensors in power networks to perform VLF (very low frequency) cable testing, which is a common practice in distribution networks and is used during commissioning tests and to support maintenance and condition monitoring of underground cables. This paper also investigates and compares the impact of different sensor technologies on the performance of cable VLF diagnostic testing.

For quality control and functionality testing, Contact Normal Forces F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> need to be measured in multi contact point connectors like MCON 8 with the least mating cycles possible. This article is about the design and the characterization of a 4-channel gage. Each channel gage consists of a metal transducer with a metal foil strain gage. The forces range from 2 N to 8 N. The gage is built-up in a modular way, connected to a data acquisition (DAQ), and its measurement system analysis MSA capability has been proven. The sensor will be implemented into the in-production quality control of the MCON 8 connector and will provide data to a global Digital Quality Inspection Plan (DQIP)-linked F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> database.

Whisker growth from metal surfaces of electrical connectors and other components has shown itself to create reliability issues. If a whisker grows in a location that bridges between two conducting surfaces not previously electrically connected, a short can occur, resulting in faulting components. The current passing through the whisker will cause its temperature to rise due to Joule Heating. This can eventually cause the whisker to melt, which can than disconnect the short circuit. Therefore whisker shorts are limited by this melting current. Previous works have examined the coupled electro-thermal model of a solid whisker conductor, but most neglect the convection of heat into air, which dissipates some of the heat, reducing the temperature rise of the whisker. This work examines the influence of convection on the melting of a shorted whisker. This is done using a finite difference model that considers the temperature dependent conductivities when solving the coupled one-dimensional heat and electrical conduction equations. Since there exists a variety of metals capable of whiskering, various metal whiskers will be modeled here for comparison purposes.

Human Activity Recognition (HAR) with body-worn sensors has been studied intensively in the past decade. Existing approaches typically rely on data from inertial sensors. This paper explores the potential of using point cloud data gathered from wearable depth cameras for on-body activity recognition. We discuss effects of different granularity in the depth information and compare their performance to inertial sensor based HAR. We evaluated our approach with a total of sixteen participants performing nine distinct activity classes in three home environments. 10-fold cross-validation results of KNN and Random Forests classification exhibit a significant increase in F-score from inertial data to depth information (by > 12 percentage points) and show a further improvement when combining low-resolution depth matrices and sensor data. We discuss the performance of the different sensor types for different contexts and show that overall, depth sensors prove to be suitable for HAR.

In this invited industry case study, Tanya X.Short introduces eight mechanisms that designers can utilize to better harness procedural character personalities in games. Tanya is co-founder and captain of Kitfox Games, a Montreal-based independent game studio, and a veteran developer and designer known for her expertise in procedural generation and systems-driven game design. With Tarn Adams, she edited the 2017 volume Procedural Generation in Game Design. As Tanya explains in this paper, there is an emerging pattern in game design that utilizes character personality as a central gameplay system. Using a number of examples spanning her experiences as both developer and player, in this paper Tanya distills her collected design knowledgeinto an actionable recipe for building better character personality systems.

The article presents the mechatronic design of the hand model. This model is powered by a pneumatic muscle system. To run a hand applied 9 muscle control movements of the fingers. The air supply muscles control system uses USB driver with digital and analog outputs. System was controlled by computer using special software made in VB6 language. Control of hands fingers is possible in manual and automation mode.

Results of a statistical analysis of transport properties measurements carried out on a number of concrete mixtures are presented. Migration and drying experiments were performed to evaluate the ionic diffusion coefficients and permeability of concrete mixtures. Seven concrete mixtures were tested. The mixtures were either batched under laboratory conditions or sampled from construction sites. For each mixture, approximately eighty concrete samples from a single batch were tested. Ionic diffusion coefficients were calculated by analyzing the evolution of the electrical current passing through concrete samples during the migration tests. Calculations took into account the volume of permeable pores of the materials and the pore solution chemistry. Permeability was calculated from mass loss measured during drying tests. The study indicates that the coefficient of variation of ionic diffusion coefficients is 11.1 % on average, with a maximum of 23.5 %, and that the coefficient of variation of permeability is 20.6 % on average, with a maximum of 37.3 %.

Context prediction is the task of inferring information about the progression of an observed context time series based on its previous behaviour. Prediction methods can be applied at several abstraction levels in the context processing chain. In a theoretical analysis as well as by means of experiments we show that the nature of the input data, the quality of the output, and finally the flow of processing operations used to make a prediction, are correlated. A comprehensive discussion of basic concepts in context prediction domains and a study on the effects of the context abstraction level on the context prediction accuracy in context prediction scenarios is provided. We develop a set of formulae that link scenario-dependent parameters to a probability for the context prediction accuracy. It is demonstrated that the results achieved in our theoretical analysis can also be confirmed in simulations as well as in experimental studies.

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