Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

Commodity-based Freight Activity on Inland Waterways through the Fusion of Public Datasets for Multimodal Transportation Planning

Within the U.S., the 18.6 billion tons of goods currently moved along the multimodal transportation system are expected to grow 51% by 2045. Most of those goods are transported by roadways. However, several benefits can be realized by shippers and consumers by shifting freight to more efficient modes, such as inland waterways, or adopting a multimodal scheme. To support such freight growth sustainably and efficiently, federal legislation calls for the development of plans, methods, and tools to identify and prioritize future multimodal transportation infrastructure needs. However, given the historical mode-specific approach to freight data collection, analysis, and modeling, challenges remain to adopt a fully multimodal approach that integrates underrepresented modes, such as waterways, into multimodal forecasting tools to identify and prioritize transportation infrastructure needs. Examples of such challenges are data heterogeneity, confidentiality, limitations in terms of spatial and temporal coverage, high cost associated with data collection, subjectivity in surveys responses, etc. To overcome these challenges, this work fuses data across a variety of novel transportation sources to close existing gaps in freight data needed to support multimodal long-range freight planning. In particular, the objective of this work is to develop methods to allow integration of inland waterway transportation into commodity-based freight forecasting models, by leveraging Automatic Identification System (AIS) data. The following approaches are presented in this dissertation: i) Maritime Automatic Identification System (AIS) data is mapped to a detailed inland navigable waterway network, allowing for an improved representation of waterway modes into multimodal freight travel demand models which currently suffer from unbalanced representation of waterways. Validation results show the model correctly identifies 84% stops at inland waterway ports and 83.5% of trips crossing locks. ii) AIS and truck Global Positioning System (GPS) data are fused to a multimodal network to identify the area of impact of a freight investment, providing a single methodology and data source to compare and contrast diverse transportation infrastructure investments. This method identifies parallel truck and vessel flows indicating potential for modal shift. iii) Truck GPS and maritime Lock Performance Monitoring System (LPMS) data are fused via a multi-commodity assignment model to characterize and quantify annual commodity throughput at port terminals on inland waterways, generating new data from public datasets, to support estimation of commodity-based freight fluidity performance measures. Results show that 84% of ports had less than a 20% difference between estimated and observed truck volumes. iv) AIS, LPMS, and truck GPS datasets are fused to disaggregate estimated annual commodity port throughput to vessel trips on inland waterways. Vessel trips characterized by port of origin, destination, path, timestamp, and commodity carried, are mapped to a detailed inland waterway network, allowing for a detailed commodity flow analysis, previously unavailable in the public domain. The novel, repeatable, data-driven methods and models proposed in this work are applied to the 43 freight port terminals located on the Arkansas River. These models help to evaluate network performance, identify and prioritize multimodal freight transportation infrastructure needs, and introduce a unique focus on modal shift towards inland waterway transportation

Probabilistic approaches for modeling text structure and their application to text-to-text generation

Since the early days of generation research, it has been acknowledged that modeling the global structure of a document is crucial for producing coherent, readable output. However, traditional knowledge-intensive approaches have been of limited utility in addressing this problem since they cannot be effectively scaled to operate in domain-independent, large-scale applications. Due to this difficulty, existing text-to-text generation systems rarely rely on such structural information when producing an output text. Consequently, texts generated by these methods do not match the quality of those written by humans – they are often fraught with severe coherence violations and disfluencies. In this chapter, I will present probabilistic models of document structure that can be effectively learned from raw document collections. This feature distinguishes these new models from traditional knowledge intensive approaches used in symbolic concept-to-text generation. Our results demonstrate that these probabilistic models can be directly applied to content organization, and suggest that these models can prove useful in an even broader range of text-to-text applications than we have considered here.National Science Foundation (U.S.) (CAREER grant IIS- 0448168)Microsoft Research. New Faculty Fellowshi

Towards an Efficient Gas Exchange Monitoring with Electrical Impedance Tomography - Optimization and validation of methods to investigate and understand pulmonary blood flow with indicator dilution

In vielen Fällen sind bei Patienten, die unter stark gestörtem Gasaustausch der Lunge leiden, die regionale Lungenventilation und die Perfusion nicht aufeinander abgestimmt. Besonders bei Patienten mit akutem Lungenversagen sind sehr heterogene räumliche Verteilungen von Belüftung und Perfusion der Lunge zu beobachten. Diese Patienten müssen auf der Intensivstation künstlich beatmet und überwacht werden, um einen ausreichenden Gasaustausch sicherzustellen. Bei schweren Lungenverletzungen ist es schwierig, durch die Anwendung hoher Beatmungsdrücke und -volumina eine optimale Balance zwischen dem Rekrutieren kollabierter Regionen zu finden, und gleichzeitig die Lunge vor weiterem Schaden durch die von außen angelegten Drücke zu schützen. Das Interesse für eine bettseitige Messung und Darstellung der regionalen Belüftungs- und Perfusionsverteilung für den Einsatz auf der Intensivstation ist in den letzten Jahren stark gestiegen, um eine lungenprotektive Beatmung zu ermöglichen und klinische Diagnosen zu vereinfachen. Die Elektrische-Impedanztomographie (EIT) ist ein nicht-invasives, strahlungsfreies und sehr mobil einsetzbares System. Es bietet eine hohe zeitliche Abtastung und eine funktionelle räumliche Auflösung, die es ermöglicht, dynamische (patho-) physiologische Prozesse zu visualisieren und zu überwachen. Die medizinische Forschung an EIT hat sich dabei hauptsächlich auf die Schätzung der räumlichen Belüftung konzentriert. Kommerziell erhältliche Systeme haben gezeigt, dass die EIT eine wertvolle Entscheidungshilfe während der mechanischen Beatmung darstellt. Allerdings ist die Abschätzung der pulmonalen Perfusion mit EIT noch nicht etabliert. Dies könnte das fehlende Glied sein, um die Analyse des pulmonalen Gasaustauschs am Krankenbett zu ermöglichen. Obwohl einige Publikationen die prinzipielle Machbarkeit der indikatorgestützten EIT zur Schätzung der räumlichen Verteilung des pulmonalen Blutflusses gezeigt haben, müssen diese Methoden optimiert und durch Vergleich mit dem Goldstandard des Lungenperfusions-Monitorings validiert werden. Darüber hinaus ist weitere Forschung notwendig, um zu verstehen welche physiologischen Informationen der EIT-Perfusionsschätzung zugrunde liegen. Mit der vorliegenden Arbeit soll die Frage beantwortet werden, ob bei der klinischen Anwendung von EIT neben der regionalen Belüftung auch räumliche Informationen des pulmonalen Blutflusses geschätzt werden können, um damit potenziell den pulmonalen Gasaustausch am Krankenbett beurteilen zu können. Die räumliche Verteilung der Perfusion wurde durch Bolusinjektion einer leitfähigen Kochsalzlösung als Indikator geschätzt, um die Verteilung des Indikators während seines Durchgangs durch das Gefäßsystem der Lunge zu verfolgen. Verschiedene dynamische EIT-Rekonstruktionsmethoden und Perfusionsparameter Schätzmethoden wurden entwickelt und verglichen, um den pulmonalen Blutfluss robust beurteilen zu können. Die geschätzten regionalen EIT-Perfusionsverteilungen wurden gegen Goldstandard Messverfahren der Lungenperfusion validiert. Eine erste Validierung wurde anhand von Daten einer tierexperimentellen Studie durchgeführt, bei der die Multidetektor-Computertomographie als vergleichende Lungenperfusionsmessung verwendet wurde. Darüber hinaus wurde im Rahmen dieser Arbeit eine umfassende präklinische Tierstudie durchgeführt, um die Lungenperfusion mit indikatorverstärkter EIT und Positronen-Emissions-Tomographie während mehrerer verschiedener experimenteller Zustände zu untersuchen. Neben einem gründlichen Methodenvergleich sollte die klinische Anwendbarkeit der indikatorgestützten EIT-Perfusionsmessung untersucht werden, indem wir vor allem die minimale Indikatorkonzentration analysierten, die eine robuste Perfusionsschätzung erlaubte und den geringsten Einfluss für den Patienten darstellt. Neben den experimentellen Validierungsstudien wurden zwei in-silico-Untersuchungen durchgeführt, um erstens die Sensitivität von EIT gegenüber des Durchgangs eines leitfähigen Indikators durch die Lunge vor stark heterogenem pulmonalen Hintergrund zu bewerten. Zweitens untersuchten wir die physiologischen Einflüsse, die zu den rekonstruierten EITPerfusionsbildern beitragen, um die Limitationen der Methode besser zu verstehen. Die Analysen zeigten, dass die Schätzung der Lungenperfusion auf der Basis der indikatorverstärkten EIT ein großes Potenzial für die Anwendung in der klinischen Praxis aufweist, da wir sie mit zwei Goldstandard-Perfusionsmesstechniken validieren konnten. Zudem konnten wertvolle Schlüsse über die physiologischen Einflüsse auf die geschätzten EIT Perfusionsverteilungen gezogen werden

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Prevention of Unauthorized Transport of Ore in Opencast Mines Using Automatic Number Plate Recognition

Security in mining is a primary concern, which mainly affects the production cost. An efficiently detecting and deterring theft will maximize the profitability of any mining organization. Many illegal transportation cases were registered in spite of rules imposed by central and state governments under Section 23 (c) of MMDR Act 1957. Use of an automated checkpoint gate based on license plate recognition and biometric fingerprint system for vehicle tracking enhances the security in mines. The method was tested on the number plates with various considerations like clean number plates, clean fingerprints, dusty and faded number plates, dusty fingerprints, and number plates captured by varying distance. By considering all the above conditions the pictures were processed by ANPR and bio-metric fingerprint modules. Vehicle license number plate was captured using a digital camera and the captured RGB image was converted to grayscale image. Thresholding was done to remove unwanted areas from the grayscale image. The characters of the number plate were segmented using Gabor filter. A track-sector matrix was generated by considering the number of pixels in each region and was matched with existing template to identify the character. The fingerprint scans the finger and matches with the template created at the time of fingerprint registration at the machine. The micro-controller accepted the processed output in binary form from ANPR and bio-metric fingerprint system. The micro-controller processed the binary output and the checkpoint gate was closed/open based on the output provided by the microcontroller to motor driver

Real-Time 3-D Motion Gesture Recognition using Kinect2 as Basis for Traditional Dance Scripting

This preliminary study presents a system capable of recognizing human gesture in real-time. The gesture is acquired from a Kinect2 sensor which provides skeleton joints represented by three-dimensional coordinate points. The model set consists of eight motion gestures is provided for basis of gesture recognition using Dynamic Time Warping (DTW) algorithm. DTW algorithm is utilized to identify in real time manner by measuring the shortest combined distances in x, y, and z coordinates in order to determined the matched gesture. It can be shown that the system is able to recognize these 8 motions in real time with some limitations. The findings of the this study will provide solid foundation of further research in which the ultimate goal of the research is to create system to automatically recognize sequence of motions in Indonesian traditional dances and convert them into standardized Resource Description Framework (RDF) scripts for the purpose of preserving these dances

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that