Semantic Innovation Management

Innovation within industrial environment can be viewed as a cyclic loop consisting of four distinct phases, i.e., recognition, initiation, implementation, and stabilization. Different information technology enabled innovation management tools supporting the lifecycle of innovation are classified as five layers, i.e., individual innovation, project innovation, collaborative innovation, distributed innovation, and semantic innovation. According the fact that the current state is evolving from distributed innovation to semantic innovation, this paper focus on the realization of Semantic Web technologies enabled semantic innovation. To explicitly and formally specify all the different perspectives of innovation related information, a shared ontology is proposed as the common language of innovation management, which describes the critical and minimal information about the innovation process in a holistic way. Then, a technical framework which employs the machine readable innovation ontology to actually improve innovation management inside an organization and among loosely coupled organizations is presented. Finally, some features of the semantic innovation are discussed

Number and plural semantics: Empirical evidence from Marori

This paper presents new empirical evidence from Marori (a Papuan language of Southern New Guinea) for the semantics of number in a complex number system. Marori has a basic three-way number system, singular/dual/plural. Marori is notable for showing distributed number exponence and constructed number strategies, in sharp contrast with familiar twoway, morphologically simpler number systems in languages such as English. Unlike in English, the reference of plurals in Marori in many contexts is to a group of three or more individuals. While Marori’s number system is typologically quite different from English, it shows an intriguing similarity in that in certain contexts, plural/nonsingular forms allow an inclusive reading (i.e. reference to any number of individuals, including one). The paper also presents evidence that all number types, including constructed dual, can be used for generic reference. The paper concludes with remarks on the theoretical significance of our findings

Finite Element Simulation of Head Impacts in Mixed Martial Arts

This study determined brain stress and strain in an unhelmeted sport and correlated this with concussive injuries. Thirteen MMA athletes were fitted with the MiG2.0 Stanford instrumented mouthguard. The mouthguard records linear acceleration and angular velocity in 6 degrees of freedom. Angular acceleration was calculated by differentiation. All events were video recorded, time stamped and reported impacts confirmed. 298 impacts above 10g were recorded during sparring sessions and 153 impacts in competitive events. The competitive events resulted in five concussions which were diagnosed by a medical doctor. The impact with the highest angular acceleration from each sparring session and competitive event was simulated using the GHBMC head and neck model. The model was run on Amazon Web Services using the LS-Dyna explicit solver. The resultant linear acceleration, strain in the corpus callosum and brain stem and shear stress in the corpus callosum were all significantly different in concussed athletes compared to uninjured. Average strain in the corpus callosum of concussed fighters was 0.27 which was 87.9% higher than uninjured fighters and was the best strain indicator of concussion. The best overall predictor of concussion found in this study was shear stress in the corpus callosum which differed by 111.4% between concussed and uninjured athletes. This study is unique in that it measured head accelerations in vivo and determined that high stress and strain in the corpus callosum correlated with the concussive injuries

Tackling overweight and obesity: Does the public health message match the science?

Background: Despite the increasing understanding of the mechanisms relating to weight loss and maintenance, there are currently no validated public health interventions that are able to achieve sustained long-term weight loss or to stem the increasing prevalence of obesity in the population. We aimed to examine the models of energy balance underpinning current research about weight-loss intervention from the field of public health, and to determine whether they are consistent with the model provided by basic science. EMBASE was searched for papers published in 2011 on weight-loss interventions. We extracted details of the population, nature of the intervention, and key findings for 27 articles.Discussion: Most public health interventions identified were based on a simple model of energy balance, and thus attempted to reduce caloric consumption and/or increase physical activity in order to create a negative energy balance. There appeared to be little consideration of homeostatic feedback mechanisms and their effect on weight-loss success. It seems that there has been a lack of translation between recent advances in understanding of the basic science behind weight loss, and the concepts underpinning the increasingly urgent efforts to reduce excess weight in the population.Summary: Public health weight-loss interventions seem to be based on an outdated understanding of the science. Their continued failure to achieve any meaningful, long-term results reflects the need to develop intervention science that is integrated with knowledge from basic science. Instead of asking why people persist in eating too much and exercising too little, the key questions of obesity research should address those factors (environmental, behavioral or otherwise) that lead to dysregulation of the homeostatic mechanism of energy regulation. There is a need for a multidisciplinary approach in the design of future weight-loss interventions in order to improve long-term weight-loss success

Coded apertures for x-ray scatter imaging

We examine coding strategies for coded aperture scatter imagers. Scatter imaging enables tomography of compact regions from snapshot measurements. We present coded aperture designs for pencil and fan beam geometries, and compare their singular value spectra with that of the Radon transform and selected volume tomography.We show that under dose constraints scatter imaging improves conditioning over alternative techniques, and that specially designed coded apertures enable snapshot 1D and 2

Learning pavement surface condition ratings through visual cues using a deep learning classification approach.

Pavement surface condition rating is an essential part of road infrastructure maintenance and asset management, and it is performed manually by the data analyst. The manual rating requires cognitive skills built through training and experience, which is quantitatively challenging and timeconsuming. This paper first analyses the complexity of the current manual visual rating system. This paper then investigates the suitability and robustness of a state-of-the-art convolutional neural network (CNN) classifier to automate the pavement surface condition index (PSCI) system used to rate pavement surfaces in Ireland. The dataset contains 3735 images of flexible asphalt pavements from Irish urban and rural environments taken from a video camera mounted in front of a van. The PSCI ratings were applied by experts using a scale of 1-10 to indicate surface conditions. The classification models are evaluated for different input pre-processing variations, image size, learning techniques, and the number of classes. Using 10 PSCI classes, the best classifier achieved a precision of 57% and a recall of 58%. Adjacent combination of classes (e.g., ratings 1 and 2 combined into a single class) to form a 5-class problem produced a classifier with a precision of 70% and recall of 77%. Given the complexity of the problem, classification using CNN holds promise as a first step towards an automated ranking system

Detecting Patches on Road Pavement Images acquired with 3D Laser Sensors using Object Detection and Deep Learning

Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This paper proposes an automatic patch detection system using object detection technique. To our knowledge, this is the first time state-of-the-art object detection models Faster RCNN, and SSD MobileNet-V2 have been used to detect patches inside images acquired by LCMS. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems. The contribution of this paper are (1) an automatic pavement patch detection models for LCMS images and (2) comparative analysis of RCNN, and SSD MobileNet-V2 models for automatic patch detection

Detecting Patches on Road Pavement Images Acquired with 3D Laser Sensors using Object Detection and Deep Learning

Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This work proposes an automatic patch detection system using an object detection technique. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems.https://arrow.tudublin.ie/cddpos/1016/thumbnail.jp