Search results for: monitoring network design

Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Saulius Minkevicius, Edvinas Greicius

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The paper is devoted to the analysis of queueing systems in the context of the network and communications theory. We investigate the inequality in an open queueing network and its applications to the theorems in heavy traffic conditions (fluid approximation, functional limit theorem, and law of the iterated logarithm) for a queue of customers in an open queueing network.

Keywords: fluid approximation, heavy traffic, models of information systems, open queueing network, queue length of customers, queueing theory

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Authors: Manpreet Kaur, Amita Rani, Sanjay Kumar

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The Internet of Things (IoT), a cyber-physical paradigm, allows a large number of devices to connect and send the sensory data in the network simultaneously. This tremendous amount of data generated leads to very high network load consequently resulting in network congestion. It further amounts to frequent loss of useful information and depletion of significant amount of nodes’ energy. Therefore, there is a need to control congestion in IoT so as to prolong network lifetime and improve the quality of service (QoS). Hence, we propose a two-level clustering based routing algorithm considering congestion score and packet priority metrics that focus on minimizing the network congestion. In the proposed Priority based Congestion Control (PBCC) protocol the sensor nodes in IoT network form clusters that reduces the amount of traffic and the nodes are prioritized to emphasize important data. Simultaneously, a congestion score determines the occurrence of congestion at a particular node. The proposed protocol outperforms the existing Packet Discard Network Clustering (PDNC) protocol in terms of buffer size, packet transmission range, network region and number of nodes, under various simulation scenarios.

Keywords: internet of things, cyber-physical systems, congestion control, priority, transmission rate

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Authors: Harris Niavis, Dimitra Politaki

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The manufacturing industry was collecting vast amounts of data for monitoring product quality thanks to the advances in the ICT sector and dedicated IoT infrastructure is deployed to track and trace the production line. However, industries have not yet managed to unleash the full potential of these data due to defective data collection methods and untrusted data storage and sharing. Blockchain is gaining increasing ground as a key technology enabler for Industry 4.0 and the smart manufacturing domain, as it enables the secure storage and exchange of data between stakeholders. On the other hand, AI techniques are more and more used to detect anomalies in batch and time-series data that enable the identification of unusual behaviors. The proposed scheme is based on smart contracts to enable automation and transparency in the data exchange, coupled with anomaly detection algorithms to enable reliable data ingestion in the system. Before sensor measurements are fed to the blockchain component and the smart contracts, the anomaly detection mechanism uniquely combines artificial intelligence models to effectively detect unusual values such as outliers and extreme deviations in data coming from them. Specifically, Autoregressive integrated moving average, Long short-term memory (LSTM) and Dense-based autoencoders, as well as Generative adversarial networks (GAN) models, are used to detect both point and collective anomalies. Towards the goal of preserving the privacy of industries' information, the smart contracts employ techniques to ensure that only anonymized pointers to the actual data are stored on the ledger while sensitive information remains off-chain. In the same spirit, blockchain technology guarantees the security of the data storage through strong cryptography as well as the integrity of the data through the decentralization of the network and the execution of the smart contracts by the majority of the blockchain network actors. The blockchain component of the Data Traceability Software is based on the Hyperledger Fabric framework, which lays the ground for the deployment of smart contracts and APIs to expose the functionality to the end-users. The results of this work demonstrate that such a system can increase the quality of the end-products and the trustworthiness of the monitoring process in the smart manufacturing domain. The proposed AI-enabled data traceability software can be employed by industries to accurately trace and verify records about quality through the entire production chain and take advantage of the multitude of monitoring records in their databases.

Keywords: blockchain, data quality, industry4.0, product quality

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Authors: Zeynep Aytaç, Nuri Yücel

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Today, as the need for high efficiency and fuel-efficient engines have increased, centrifugal compressor designs are expected to be high-efficient and have high-pressure ratios than ever. The present study represents a design methodology of centrifugal compressor placed in a mini jet engine for the design and off-design points with the utilization of computational fluid dynamics (CFD) and compares the performance characteristics at the mentioned two points. Although the compressor is expected to provide the required specifications at the design point, it is known that it is important for the design to deliver the required parameters at the off-design point also as it will not operate at the design point always. It was observed that the obtained mass flow rate, pressure ratio, and efficiency values are within the limits of the design specifications for the design and off-design points. Despite having different design inputs for the mentioned two points, they reveal similar flow characteristics in the general frame.

Keywords: centrifugal compressor, computational fluid dynamics, design point, off-design point

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Authors: Noman Shabbir, Roheel Nawaz, Muhammad N. Iqbal, Junaid Zafar

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This paper focuses on the performance evaluation of routing protocols in WLAN based Wireless Sensor Networks (WSNs). A comparative analysis of routing protocols such as Ad-hoc On-demand Distance Vector Routing System (AODV), Dynamic Source Routing (DSR) and Optimized Link State Routing (OLSR) is been made against different network parameters like network load, end to end delay and throughput in small, medium and large-scale sensor network scenarios to identify the best performing protocol. Simulation results indicate that OLSR gives minimum network load in all three scenarios while AODV gives the best throughput in small scale network but in medium and large scale networks, DSR is better. In terms of delay, OLSR is more efficient in small and medium scale network while AODV is slightly better in large networks.

Keywords: WLAN, WSN, AODV, DSR, OLSR

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: neural networks, pattern learning, security, wireless sensor networks

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Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies indicate that a large fronto-parieto-temporal network support nouns and their features, with some areas store semantic knowledge (visual, auditory, olfactory, gustatory,…), other areas store lexical representation and other areas are implicated in general semantic processing. However, it is not well understood how this fronto-parieto-temporal network can be modulated by different semantic tasks and different semantic relations between nouns. In this study, we combine a behavioral semantic network, functional MRI studies involving object’s related nouns and brain network studies to explain how different semantic tasks and different semantic relations between nouns can modulate the activity within the brain network of nouns and their features. We first describe how nouns and their features form a large scale brain network. For this end, we examine the connectivities between areas recruited during the processing of nouns to know which configurations of interaction areas are possible. We can thus identify if, for example, brain areas that store semantic knowledge communicate via functional/structural links with areas that store lexical representations. Second, we examine how this network is modulated by different semantic tasks involving nouns and finally, we examine how category specific activation may result from the semantic relations among nouns. The results indicate that brain network of nouns and their features is highly modulated and flexible by different semantic tasks and semantic relations. At the end, this study can be used as a guide to help neurosientifics to interpret the pattern of fMRI activations detected in the semantic processing of nouns. Specifically; this study can help to interpret the category specific activations observed extensively in a large number of neuroimaging studies and clinical studies.

Keywords: nouns, features, network, category specificity

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Authors: Ozgun Oktar, Sevilay Can, Cengiz V. Ekici

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The oil spill is a form of pollution caused by releasing of a liquid petroleum hydrocarbon into the marine environment. Considering the growth of ship traffic, increasing of off-shore oil drilling and seaside refineries affect the risk of oil spill upward. The oil spill is easy to spread to large areas when occurs especially on the sea surface. Remote sensing technology offers the easiest way to control/monitor the area of the oil spill in a large region. It’s usually easy to detect pollution when occurs by the ship accidents, however monitoring non-accidental pollution could be possible by remote sensing. It is also needed to observe specific regions daily and continuously by satellite solutions. Remote sensing satellites mostly and effectively used for monitoring oil pollution are RADARSAT, ENVISAT and MODIS. Spectral coverage and transition period of these satellites are not proper to monitor Marmara Sea and Istanbul Strait continuously. In this study, RASAT and GOKTURK-2 are suggested to use for monitoring Marmara Sea and Istanbul Strait. RASAT, with spectral resolution 420 – 730 nm, is the first Turkish-built satellite. GOKTURK-2’s resolution can reach up to 2,5 meters. This study aims to analyze the images from both satellites and produce maps to show the regions which have potentially affected by spills from shipping traffic.

Keywords: Marmara Sea, monitoring, oil spill, satellite remote sensing

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Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

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Authors: G. Santamato, M. Solazzi, A. Frisoli

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Systems for detection of damages in railway pantographs effectively reduce the cost of maintenance and improve time scheduling. In this paper, we present an approach to design a monitoring tool fitting strong customer requirements such as portability and ease of use. Pantograph has been modeled to estimate its dynamical properties, since no data are available. With the aim to focus on suspensions health, a two Degrees of Freedom (DOF) scheme has been adopted. Parameters have been calculated by means of analytical dynamics. A Finite Element Method (FEM) modal analysis verified the former model with an acceptable error. The detection strategy seeks phase-plots topology alteration, induced by defects. In order to test the suitability of the method, leakage in the dashpot was simulated on the lumped model. Results are interesting because changes in phase plots are more appreciable than frequency-shift. Further calculations as well as experimental tests will support future developments of this smart strategy.

Keywords: pantograph models, phase plots, structural health monitoring, damage detection

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Authors: Xiaohan Bookman, Xiaoyan Zhu

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Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labeled samples and flexible network. We have conducted experiments on four UCI open data sets and achieved good results as expected. In conclusion, our model could handle more sparse labeled and more high-dimension data set in a high accuracy while modification from old ANN method to our method is easy and almost free of work.

Keywords: Max-Margin Principle, Feed-Forward Neural Network, classifier, structural risk

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Authors: A. Clementking, C. Jothi Venkateswaran

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Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources.

Keywords: master-slave back propagation neural network model(MSBPNNM), water quality analysis, multivariate analysis, environmental mining

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Authors: Henni Mansour Abdelwaheb

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This study discusses the design of a bounded position/force feedback controller developed to ensure position and force tracking for bilateral teleoperation arms operating with variable delay, and actuator saturation. Also, an adaptive robust Radial Basis Function (RBF) neural network is used to estimate the environment torque. The parameters of the environment torque are then sent from the slave site to the master site as a non-power signal to avoid passivity problems. Moreover, a nonlinear function is applied to each controller term as a smooth saturation function, providing a bounded control signal and preserving the system’s actuators. Lastly, the Lyapunov approach demonstrates the global stability of the controlled system, and numerical experiment results further confirm the validity of the presented strategy.

Keywords: teleoperation manipulators system, time-varying delay, actuator saturation, adaptive robust rbf neural network approximation, uncertainties

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Authors: Christodoulou Christos, Politis Anastasios

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The utilization of Wireless Body Area Network (WBAN) is experiencing a notable surge in popularity as a result of its widespread implementation in the field of smart health. WBANs utilize small sensors implanted within the human body to monitor and record physiological indicators. These sensors transmit the collected data to hospitals and healthcare facilities through designated access points. Bio-sensors exhibit a diverse array of shapes and sizes, and their deployment can be tailored to the condition of the individual. Multiple sensors may be strategically placed within, on, or around the human body to effectively observe, record, and transmit essential physiological indicators. These measurements serve as a basis for subsequent analysis, evaluation, and therapeutic interventions. In conjunction with physical health concerns, numerous smartwatches are engineered to employ artificial intelligence techniques for the purpose of detecting mental health conditions such as depression and anxiety. The utilization of smartwatches serves as a secure and cost-effective solution for monitoring mental health. Physiological signals are widely regarded as a highly dependable method for the recognition of emotions due to the inherent inability of individuals to deliberately influence them over extended periods of time. The techniques that WBANs employ to recognize emotions are thoroughly examined in this article.

Keywords: emotion recognition, wireless body area network, WBAN, ERC, wearable devices, psychological signals, emotion, smart-watch, prediction

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Authors: Gholam Reza Parvizi, Ali Reza Kargar, Amir Arani

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In the present days, learning and teaching researchers have emphasized the role which teachers, tutors, and trainers’ constraint knowledge treat in resizing and trimming what they perform in educational atmosphere. Regarding English language as subject to teaching, although the prominence of instructor’s knowledge about grammar has also been stressed, but the lack of empirical insights into the relationship between teacher’ self-monitoring and self-response of grammar knowledge have been observed. With particular attention to the grammar this article indicates and discusses information obtained self- feedback and conversing teachers of a kind who backwash the issue. The result of the study indicates that enabling teachers to progress and maintain a logical and realistic awareness of their knowledge about grammar have to be prominent goal for teachers’ education and development programs.

Keywords: grammar knowledge, self-monitoring, self-response, teaching grammar, language teaching program

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Authors: Ioan Corneliu Salisteanu, Valentin Dogaru Ulieru, Mihaita Nicolae Ardeleanu, Alin Pohoata, Bogdan Salisteanu, Stefan Broscareanu

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Creating a personalized statistic for an individual within the population using IT systems, based on the searches and intercepted spheres of interest they manifest, is just one 'atom' of the artificial intelligence analysis network. However, having the ability to generate statistics based on individual data intercepted from large demographic areas leads to reasoning like that issued by a human mind with global strategic ambitions. The DiaMOTO device is a technical sensory system that allows the interception of car events caused by a driver, positioning them in time and space. The device's connection to the vehicle allows the creation of a source of data whose analysis can create psychological, behavioural profiles of the drivers involved. The SiaMOTO system collects data from many vehicles equipped with DiaMOTO, driven by many different drivers with a unique fingerprint in their approach to driving. In this paper, we aimed to explain the software infrastructure of the SiaMOTO system, a system designed to monitor and improve driver driving behaviour, as well as the criteria and algorithms underlying the intelligent analysis process.

Keywords: artificial intelligence, data processing, driver behaviour, driver monitoring, SiaMOTO

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Authors: Subhash Reddy

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Mobile communication in today’s world means a lot to the human kind, through this many deals are made and others are broken, within seconds. That is because of our sophisticated methods of transporting the data at very high speeds and to very long distances, within no time. That is also because we kept on changing the method of serving the connections as the no of connections kept on increasing, that has led to many methods like TDMA, CDMA, and FDMA, etc. in wireless communications. And also the areas, where the connections are provided are also divided into CELLS, which are the basic blocks for cellular communications. Along with the wireless network, providing a wired network in mobile phones would serve as a very good alternative and would divert the extra traffic of a cell, so that a CELL which is providing wireless network can operate more efficiently.

Keywords: CDMA, FDMA, TDMA, CELL

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Authors: Luís Pina

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The conservation of marine biodiversity keeps ecosystems in balance and ensures the sustainable use of resources. In this context, technological resources have been used for monitoring marine species to allow biologists to obtain data in real-time. There are different mobile applications developed for data collection for monitoring purposes, but these systems are designed to be utilized only on third-generation (3G) phones or smartphones with Internet access and in rural parts of the developing countries, Internet services and smartphones are scarce. Thus, the objective of this work is to develop a system to monitor marine turtles using Unstructured Supplementary Service Data (USSD), which users can access through basic mobile phones. The system aims to improve the data collection mechanism and enhance the effectiveness of current systems in monitoring sea turtles using any type of mobile device without Internet access. The system will be able to report information related to the biological activities of marine turtles. Also, it will be used as a platform to assist marine conservation entities to receive reports of illegal sales of sea turtles. The system can also be utilized as an educational tool for communities, providing knowledge and allowing the inclusion of communities in the process of monitoring marine turtles. Therefore, this work may contribute with information to decision-making and implementation of contingency plans for marine conservation programs.

Keywords: GSM, marine biology, marine turtles, unstructured supplementary service data (USSD)

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Authors: Giovanni Menichini, Salvatore Giacomo Morano, Gloria Terenzi, Luca Salvatori, Maurizio Orlando

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The “Guidelines for Risk Classification, Safety Assessment, and Structural Health Monitoring of Existing Bridges” were recently approved by the Italian Government to define technical standards for managing the national network of existing bridges. These guidelines provide a framework for risk mitigation and safety assessment of bridges, which are essential elements of the built environment and form the basis for the operation of transport systems. Within the guideline framework, a workflow based on three main points was proposed: (1) risk-based, i.e., based on typical parameters of hazard, vulnerability, and exposure; (2) multi-level, i.e., including six assessment levels of increasing complexity; and (3) multirisk, i.e., assessing structural/foundational, seismic, hydrological, and landslide risks. The paper focuses on applying the Italian Guidelines to specific case studies, aiming to identify the parameters that predominantly influence the determination of the “class of attention”. The significance of each parameter is determined via sensitivity analysis. Additionally, recommendations for enhancing the process of assigning the class of attention are proposed.

Keywords: bridge safety assessment, Italian guidelines implementation, risk classification, structural health monitoring

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Authors: Tammam A. Benmusa, Mohamed A. Shlibek, Rawad M. Swesi

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The investigation of Buildings Penetration Loss (BPL) of radio signal is getting more and more important. It plays an important role in calculating the indoor coverage for wireless communication networks. In this paper, the theory behind BPL and its mechanisms have been reviewed. The operating frequency, coverage area type, climate condition, time of measurement, and other factors affecting the values of BPL have been discussed. The practical part of this work was conducting 4000 measurements of BPL in different areas in the Libyan capital, Tripoli, to get empirical model for this loss. The measurements were taken for 2 different types of wireless communication networks; mobile telephone network (for Almadar company), which operates at 900 MHz and WiMAX network (LTT company) which operates at 2500 MHz. The results for each network were summarized and presented in several graphs. The graphs are showing how the BPL affected by: time of measurement, morphology (type of area), and climatic environment.

Keywords: building penetration loss, wireless network, mobile network, link budget, indoor network performance

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Danying Gu, Xiaoyan Li, Yuanlei He

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As a knowledge-intensive industry, nuclear industry highly values the importance of safety and quality. The life cycle of a NPP (Nuclear Power Plant) can last 100 years from the initial research and design to its decommissioning. How to implement the high-quality knowledge management and how to contribute to a more safe, advanced and economic NPP (Nuclear Power Plant) is the most important issue and responsibility for knowledge management. As the lead of nuclear industry, nuclear research and design institute has competitive advantages of its advanced technology, knowledge and information, DKM (Design Knowledge Management) of nuclear research and design institute is the core of the knowledge management in the whole nuclear industry. In this paper, the study and practice on DKM and knowledge transfer across the life cycle of a new-built NPP in China is introduced. For this digital intelligent NPP, the whole design process is based on a digital design platform which includes NPP engineering and design dynamic analyzer, visualization engineering verification platform, digital operation maintenance support platform and digital equipment design, manufacture integrated collaborative platform. In order to make all the design data and information transfer across design, construction, commissioning and operation, the overall architecture of new-built digital NPP should become a modern knowledge management system. So a digital information transfer model across the NPP life cycle is proposed in this paper. The challenges related to design knowledge transfer is also discussed, such as digital information handover, data center and data sorting, unified data coding system. On the other hand, effective delivery of design information during the construction and operation phase will contribute to the comprehensive understanding of design ideas and components and systems for the construction contractor and operation unit, largely increasing the safety, quality and economic benefits during the life cycle. The operation and maintenance records generated from the NPP operation process have great significance for maintaining the operating state of NPP, especially the comprehensiveness, validity and traceability of the records. So the requirements of an online monitoring and smart diagnosis system of NPP is also proposed, to help utility-owners to improve the safety and efficiency.

Keywords: design knowledge management, digital nuclear power plant, knowledge transfer, life cycle

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Authors: Shankaran Sitarama

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New Product Development (NPD) is invariably a team effort and involves effective teamwork. NPD team has members from different disciplines coming together and working through the different phases all the way from conceptual design phase till the production and product roll out. Creativity and Innovation are some of the key factors of successful NPD. Team members going through the different phases of NPD interact and work closely yet challenge each other during the design phases to brainstorm on ideas and later converge to work together. These two traits require the teams to have a divergent and a convergent thinking simultaneously. There needs to be a good balance. The team dynamics invariably result in conflicts among team members. While some amount of conflict (ideational conflict) is desirable in NPD teams to be creative as a group, relational conflicts (or discords among members) could be detrimental to teamwork. Team communication truly reflect these tensions and team dynamics. In this research, team communication (emails) between the members of the NPD teams is considered for analysis. The email communication is processed through a semantic analysis algorithm (LSA) to analyze the content of communication and a semantic similarity analysis to arrive at a social network graph that depicts the communication amongst team members based on the content of communication. The amount of communication (content and not frequency of communication) defines the interaction strength between the members. Social network adjacency matrix is thus obtained for the team. Standard social network analysis techniques based on the Adjacency Matrix (AM) and Dichotomized Adjacency Matrix (DAM) based on network density yield network graphs and network metrics like centrality. The social network graphs are then rendered for visual representation using a Metric Multi-Dimensional Scaling (MMDS) algorithm for node placements and arcs connecting the nodes (representing team members) are drawn. The distance of the nodes in the placement represents the tie-strength between the members. Stronger tie-strengths render nodes closer. Overall visual representation of the social network graph provides a clear picture of the team’s interactions. This research reveals four distinct patterns of team interaction that are clearly identifiable in the visual representation of the social network graph and have a clearly defined computational scheme. The four computational patterns of team interaction defined are Central Member Pattern (CMP), Subgroup and Aloof member Pattern (SAP), Isolate Member Pattern (IMP), and Pendant Member Pattern (PMP). Each of these patterns has a team dynamics implication in terms of the conflict level in the team. For instance, Isolate member pattern, clearly points to a near break-down in communication with the member and hence a possible high conflict level, whereas the subgroup or aloof member pattern points to a non-uniform information flow in the team and some moderate level of conflict. These pattern classifications of teams are then compared and correlated to the real level of conflict in the teams as indicated by the team members through an elaborate self-evaluation, team reflection, feedback form and results show a good correlation.

Keywords: team dynamics, team communication, team interactions, social network analysis, sna, new product development, latent semantic analysis, LSA, NPD teams

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Authors: Jifang Tao, Fei Gao, Hong Cai, Yuan Jin Zheng, Yuan Dong Gu

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Photoacoustic technology is used to measure effect absorption of a light by means of acoustic detection, which provides a high sensitive, low-cross response, cost-effective solution for gas molecular detection. In this paper, we proposed an integrated photoacoustic sensor for Indoor-air quality (IAQ) monitoring. The sensor consists of an acoustically resonant cavity, a high silicon acoustic transducer chip, and a low-cost light source. The light is modulated at the resonant frequency of the cavity to create an enhanced periodic heating and result in an amplified acoustic pressure wave. The pressure is readout by a novel acoustic transducer with low noise. Based on this photoacoustic sensor, typical indoor gases, including CO2, CO, O2, and H2O have been successfully detected, and their concentration are also evaluated with very high accuracy. It has wide potential applications in IAQ monitoring for agriculture, food industry, and ventilation control systems used in public places, such as schools, hospitals and airports.

Keywords: indoor-air quality (IAQ) monitoring, photoacoustic gas sensor, cavity enhancement, integrated gas sensor

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Authors: Peter Fedichev

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We studied fundamental aspects of aging to develop a mathematical model of gene regulatory network. We show that aging manifests itself as an inherent instability of gene network leading to exponential accumulation of regulatory errors with age. To validate our approach we studied age-dependent omic data such as transcriptomes, metabolomes etc. of different model organisms and humans. We build a computational platform based on our model to identify the targets and biomarkers of aging to design new drugs against aging and age-related diseases. As biomarkers of aging, we choose the rate of aging and the biological age since they completely determine the state of the organism. Since rate of aging rapidly changes in response to an external stress, this kind of biomarker can be useful as a tool for quantitative efficacy assessment of drugs, their combinations, dose optimization, chronic toxicity estimate, personalized therapies selection, clinical endpoints achievement (within clinical research), and death risk assessments. According to our model, we propose a method for targets identification for further interventions against aging and age-related diseases. Being a biotech company, we offer a complete pipeline to develop an anti-aging drug-candidate.

Keywords: aging, longevity, biomarkers, senescence

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Authors: Dweepabiswa Bagchi, D. V. Senthilkumar

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Numerous studies have typically demonstrated the devastation of invasions on an isolated ecosystem or, at most, a network of dispersively coupled similar ecosystem patches. Using such a simplistic 2-D network model, one can only consider dispersal coupling and inter-species trophic interactions. However, in a realistic ecosystem, numerous species co-exist and interact trophically and non-trophically in groups of 2 or more. Even different types of dispersal can introduce complexity in an ecological network. Therefore, a more accurate representation of actual ecosystems (or ecological networks) is a complex network consisting of motifs formed by two or more interacting species. Here, the apropos structure of the network should be multiplex or multi-layered. Motifs between different patches or species should be identical within the same layer and vary from one layer to another. This study investigates three distinct ecological multiplex networks facing invasion from one or more external species. This work determines and quantifies the criteria for the increased extinction risk of these networks. The dynamical states of the network with high extinction risk, i.e., the danger states, and those with low extinction risk, i.e., the resistive network states, are both subsequently identified. The analysis done in this study further quantifies the persistence of the entire network corresponding to simultaneous changes in the strength of invasive dispersal and higher-order trophic and non-trophic interactions. This study also demonstrates that the ecosystems enjoy an inherent advantage against invasions due to their multiplex network structure.

Keywords: increased ecosystem persistence, invasion on ecosystems, multiplex networks, non-trophic interactions

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Authors: Chia-Chi Chang

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Most battery-driven wireless sensor devices will enter in sleep mode as soon as possible to extend the overall lifetime of a sensor network. It is necessary to turn off unnecessary radio and peripheral functions, especially the radio unit always consumes more energy than other components during wireless communication. The microcontroller is the most important part of the wireless sensor device. It is responsible for the manipulation of sensing data and communication protocols. The microcontroller always has different sleep modes, each with a different level of energy usage. The deeper the sleep, the lower the energy consumption. Most wireless sensor devices can only enter the sleep mode: the external low-frequency oscillator is still running to wake up the sleeping microcontroller when the sleep timer expires. In this paper, our sensor device can enter the extended sleep mode: none of the oscillator is running and the wireless sensor device has the nanoampere consumption and self-awaking ability. Finally, these wireless sensor devices were deployed in a smart home security network.

Keywords: wireless sensor network, battery-driven, sleep mode, home security

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Authors: Hamid R. Lashgarian Azad, Seyed N. Shetab Boushehri

Abstract:

Wireless sensor networks (WSN) have become progressively popular due to their wide range of applications. Wireless Sensor Network is made of numerous tiny sensor nodes that are battery-powered. It is a very significant problem to maximize the lifetime of wireless sensor networks. In this paper, we propose a two-stage protocol based on an imperialist competitive algorithm (2S-ICA) to solve a sensor network optimization problem. The energy of the sensors can be greatly reduced and the lifetime of the network reduced by long communication distances between the sensors and the sink. We can minimize the overall communication distance considerably, thereby extending the lifetime of the network lifetime through connecting sensors into a series of independent clusters using 2SICA. Comparison results of the proposed protocol and LEACH protocol, which is common to solving WSN problems, show that our protocol has a better performance in terms of improving network life and increasing the number of transmitted data.

Keywords: wireless sensor network, imperialist competitive algorithm, LEACH protocol, k-means clustering

Procedia PDF Downloads 91

Authors: Caglayan Hizal, Kutay Yuceturk, Ertugrul Turker Uzun, Hasan Ceylan, Engin Aktas, Gursoy Turan

Abstract:

Monitoring concepts for structural systems have been investigated by researchers for decades since such tools are quite convenient to determine intervention planning of structures. Despite the considerable development in this regard, the efficient use of monitoring data in reliability assessment, and prediction models are still in need of improvement in their efficiency. More specifically, reliability-based seismic risk assessment of engineering structures may play a crucial role in the post-earthquake decision-making process for the structures. After an earthquake, professionals could identify heavily damaged structures based on visual observations. Among these, it is hard to identify the ones with minimum signs of damages, even if they would experience considerable structural degradation. Besides, visual observations are open to human interpretations, which make the decision process controversial, and thus, less reliable. In this context, when a continuous monitoring system has been previously installed on the corresponding structure, this decision process might be completed rapidly and with higher confidence by means of the observed data. At this stage, the Structural Health Monitoring (SHM) procedure has an important role since it can make it possible to estimate the system reliability based on a recursively updated mathematical model. Therefore, integrating an SHM procedure into the reliability assessment process comes forward as an important challenge due to the arising uncertainties for the updated model in case of the environmental, material and earthquake induced changes. In this context, this study presents a case study on SHM-integrated reliability assessment of the continuously monitored progressively damaged systems. The objective of this study is to get instant feedback on the current state of the structure after an extreme event, such as earthquakes, by involving the observed data rather than the visual inspections. Thus, the decision-making process after such an event can be carried out on a rational basis. In the near future, this can give wing to the design of self-reported structures which can warn about its current situation after an extreme event.

Keywords: condition assessment, vibration-based SHM, reliability analysis, seismic risk assessment

Procedia PDF Downloads 138