Combustion monitoring in engines using accelerometer signals

Recent regulations for emission control from combustion engines have led to focus on various methods to monitor control and the combustion process. The presented work explores the potential relationship between various combustion events monitored using in-cylinder pressure transducer and the resulting block vibration measured using accelerometers. Various features of combustion development process were derived from the vibration data acquired. The methodology was analyzed using a single cylinder diesel engine. The heat release data was able to detect and extracted various indicators of the combustion process like start of combustion and half of injected fuel positions. Higher correlation was observed for the data computed using heat release curve obtained from the in-cylinder pressure measurements and those obtained using the accelerometer signals. The proposed methodology can be used to optimize the fuel injection timing in order to reduce emissions from engines to comply with various regulations. The practical importance of presented work demonstrates use of monitoring of block vibration signals as a mean of non-intrusive methods of diagnosis of engine for its effective condition monitoring

Common fixed points for self-mappings on partial metric spaces

In this paper, we prove some results of a common fixed point for two self-mappings on partial metric spaces. Our results generalize some interesting results of Ilic et al. (Appl. Math. Lett. 24:1326-1330, 2011). We conclude with a result of the existence of a fixed point for set-valued mappings in the context of 0-complete partial metric spaces

Energy-based Adaptive Compression in Water Network Control Systems

© 2016 IEEE.Contemporary water distribution networks exploit Internet of Things (IoT) technologies to monitor and control the behavior of water network assets. Smart meters/sensor and actuator nodes have been used to transfer information from the water network to data centers for further analysis. Due to the underground position of water assets, many water companies tend to deploy battery driven nodes which last beyond the 10-year mark. This prohibits the use of high-sample rate sensing therefore limiting the knowledge we can obtain from the recorder data. To alleviate this problem, efficient data compression enables high-rate sampling, whilst reducing significantly the required storage and bandwidth resources without sacrificing the meaningful information content. This paper introduces a novel algorithm which combines the accuracy of standard lossless compression with the efficiency of a compressive sensing framework. Our method balances the tradeoffs of each technique and optimally selects the best compression mode by minimizing reconstruction errors, given the sensor node battery state. To evaluate our algorithm, real high-sample rate water pressure data of over 170 days and 25 sensor nodes of our real world large scale testbed was used. The experimental results reveal that our algorithm can reduce communication around 66% and extend battery life by 46% compared to traditional periodic communication techniques

Frequency dispersion reduction and bond conversion on n-type GaAs by in situ surface oxide removal and passivation

The method of surface preparation on n-type GaAs, even with the presence of an amorphous-Si interfacial passivation layer, is shown to be a critical step in the removal of accumulation capacitance frequency dispersion. In situ deposition and analysis techniques were used to study different surface preparations, including NH4OH, Si-flux, and atomic hydrogen exposures, as well as Si passivation depositions prior to in situ atomic layer deposition of Al2O3. As–O bonding was removed and a bond conversion process with Si deposition is observed. The accumulation capacitance frequency dispersion was removed only when a Si interlayer and a specific surface clean were combined

GaAs interfacial self-cleaning by atomic layer deposition

The reduction and removal of surface oxides from GaAs substrates by atomic layer deposition (ALD) of Al2O3 and HfO2 are studied using in situ monochromatic x-ray photoelectron spectroscopy. Using the combination of in situ deposition and analysis techniques, the interfacial "self-cleaning" is shown to be oxidation state dependent as well as metal organic precursor dependent. Thermodynamics, charge balance, and oxygen coordination drive the removal of certain species of surface oxides while allowing others to remain. These factors suggest proper selection of surface treatments and ALD precursors can result in selective interfacial bonding arrangements

Neurological Disorders and Publication Abstracts Follow Elements of Social Network Patterns when Indexed Using Ontology Tree-Based Key Term Search

Disorders of the Central Nervous System (CNS) are worldwide causes of morbidity and mortality. In order to further investigate the nature of the CNS research, we generate from an initial reference a controlled vocabulary of CNS disorder-related terms and ontological tree structure for this vocabulary, and then apply the vocabulary in an analysis of the past ten years of abstracts (N = 10,488) from a major neuroscience journal. Using literal search methodology with our terminology tree, we find over 5,200 relationships between abstracts and clinical diagnostic topics. After generating a network graph of these document-topic relationships, we find that this network graph contains characteristics of document-author and other human social networks, including evidence of scale-free and power law-like node distributions. However, we also found qualitative evidence for Z-normal-type (albeit logarithmically skewed) distributions within disorder popularity. Lastly, we discuss potential consumer-centered as well as clinic-centered uses for our ontology and search methodology