The Multidisciplinary Study on Culture and Personality Determined by the Natural Environment and Its Changes

At present, the natural disasters faced by Sri Lankans are increasing day by day. Even in a catastrophic situation, it becomesintegral part of cultural life when in constant conflict with life. Rather than finding solution to the problem, the study based onthe persuasiveness of people to make it a blessing to their day to day lives and the nature of the new lifestyles associated with it. The objective of the research was to study the socio-cultural impact of the natural environment and its changes. According to theanalytical research, the study area was selected as 435- Kahatapitiya Gramasewa Division located in Hanwella Divisional Secretariat Division. Out of the 344 indoor units the population that suffering from flood, among the population 40 householdswere selected and studied as 12%. The sample was selected through the judging sample, stratification sample and randomsample. The findings reveal that the whole socio-cultural environment such as the economy, daily life and religious beliefs ofthe people living in the area are adapted to this environment. Examples including surrendering to the god Ranwala, buildinghouses to withstand disasters and taking advantage of disasters. 58% of the respondents were of the opinion that this disaster was a common situation faced by all the people living in the lowlands and that no action could be taken to address it. Thus, it can be concluded that the socio-cultural environment in which he is grew up as well as the environment in which he brought up creates the whole personality, including the inner status of the individual. However, it should be noted that the natural environment and the natural disasters are great impact on it have a great effect on the social status, cultural background as well as the personality of the people living under that environmental background. DOI : http://doi.org/10.31357/fhss/vjhss.v06i02.0

Pulsed Eddy Current Sensing for Condition Assessment of Reinforced Concrete

© 2019 IEEE. Reinforced concrete (i.e., concrete wall-like structures having steel reinforcement rods embedded within) are commonly available as civil infrastructures. Such concrete structures, especially the walls of sewers, are vulnerable to bacteria and gas induced acid attacks which contribute to deterioration of the concrete and subsequent concrete wall loss. Therefore, quantification of concrete wall loss becomes important in determining the health and structural integrity of concrete walls. An effective strategy that can be formulated to quantify concrete wall loss is, locating a reinforcement rod and determining the thickness of concrete overlaying the rod via Non-destructive Testing and Evaluation (NDT E). Pulsed Eddy Current (PEC) sensing is commonly used for NDT E of metallic structures, including ferromagnetic materials. Since steel reinforcement rods that are commonly embedded in concrete also are ferromagnetic, this paper contributes by presenting experimental results, which suggest the usability of PEC sensing for reinforced concrete assessment, via executing the aforementioned strategy

Frequency sweep based sensing technology for non-destructive electrical resistivity measurement of concrete

© 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved. Electrical resistivity is an important parameter to be monitored for the conditional assessment and health monitoring of aging and new concrete infrastructure. In this paper, we report the design and development of a frequency sweep based sensing technology for non-destructive electrical resistivity measurement of concrete. Firstly, a sensing system prototype was developed based on the Wenner probe arrangement for the electrical resistivity measurements. This system operates by integrating three major units namely current injection unit, sensing unit and microcontroller unit. Those units govern the overall operations of the sensing system. Secondly, the measurements from the developed unit were compared with the measurements of the commercially available device at set conditions. This experimentation evaluated the measurement performance and demonstrated the effectiveness of the developed sensor prototype. Finally, the influence of rebar and the effect of frequency on the electrical measurements were studied through laboratory experimentation on a concrete sample. Experimental results indicated that the electrical resistivity measurements taken at a closer proximity to the rebar had its influence than the measurements taken away from the rebar in the ideal set condition. Also, the increase in electrical resistivity to the increase in frequency was observed, and then the measurements show lesser variations to higher frequency inputs

Fast wavelet transform domain texture synthesis

Block based texture synthesis algorithms have shown better results than others as they help to preserve the global structure. Previous research has proposed several approaches in the pixel domain, but little effort has been taken in the synthesis of texture in a multiresolution domain. We propose a multiresolution framework in which coefficient-blocks of the spatio-frequency components of the input texture are efficiently stitched together to form the corresponding components of the output texture. We propose two algorithms to this effect. In the first, we use a constant block size throughout the algorithm. In the second, we adaptively split blocks so as to use the largest possible block size in order to preserve the global structure, while maintaining the mismatched error of the overlapped boundaries below a certain error tolerance. Special consideration is given to minimization of the computational cost, throughout the algorithm designs. We show that the adaptation of the multiresolution approach results in a fast, cost-effective, flexible texture synthesis algorithm that is capable of being used in modern, bandwidth-adaptive, real-time imaging applications. A collection of regular and stochastic test textures is used to prove the effectiveness of the proposed algorithm

A Study on the Trends of Rainfall Patterns in the Intermediate and Dry Zones of Sri Lanka A Comparative Study for the Periods Ranging from 1941-1970 and 1971-2000

Since Sri Lanka is a tropical island unstable nature of the tropics has madeseveral temporal and spatial variations in rainfall throughout the island. Rainfall is oneof the principal factors that has been used to identify the three broad climatic zones inSri Lanka, namely the Wet zone, Intermediate zone and Dry zone. Much of thescientific researches on the rainfall pattern in Sri Lanka have revealed that most of themeteorological stations had recorded decreasing trends of rainfall during the past 100years. The present study attempts to ascertain the validity of these findings withreference to the study area of Intermediate and Dry zones of Sri Lanka (hereafterreferred to as the Intermediate and Dry zones). Considering the agricultural economy,the Dry and the Intermediate zones have been contributing towards more than 90% ofthe islands paddy. However, these two climatic zones show water surpluses in onlythree months (October to December) of the year. With this brief background, the presentstudy aims to identify the trends in rainfall in the Intermediate and the Dry Zones.Micro level framework is used for the selection of rainfall reporting stations andagro-ecological regions of these two zones. Accordigly 14 rainfall reporting stationshave selected for the study. Further, this is a comparative study of two 30 year periodsranging from 1941-1970 (1st period) and 1971-2000 (2nd period) and its seasons (FirstInter Monsoon (FIM), South West Monsoon (SEM), Second Inter Monsoon (SIM) andNorth East Monsoon (NEM). Time series analysis is employed for the identification ofany positive or negative trends of rainfall and the analysis is done on annual andseasonal basis.The results obtained from the analysis revealed that the highest and the lowestpositive trends belong to the 2nd period. It is clear that both highest and lowest negativetrends are apparent in the 1st period. All positive trends of the FIM in the 1st period havechanged into negative trends in the 2nd period. During the SWM, the highest positivetrend is showed in the 2nd period.Keywords: Rainfall Pattern, Intermediate Zone, Dry Zone, Positive Trend, NegativeTren

Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization

Sensors help robots perceive their environment and localize themselves. Determining a robot's location requires a range of sensing systems. Depending on accuracy criteria and navigation conditions, robot localization sensors can differ. Common sensors for robot localization include encoders, GPS, cameras, LIDARs, and IMUs. Traditional sensors are not capable enough in changing environments and uneven terrain. In this paper, we propose a method based on deep learning to use the subsurface features obtained through a Ground Penetrating Radar (GPR) to estimate the odometry of a robot. This proposed method does not rely on visual features or the distance gathered from wheel encoders. The proposed approach was evaluated on a publicly available dataset, and the evaluation results show that the proposed method can be used for robot localization without the need for odometry from wheel encoders

Real-Time Monitoring and Driver Feedback to Promote Fuel Efficient Driving

Improving the fuel efficiency of vehicles is imperative to reduce costs andprotect the environment. While the efficient engine and vehicle designs, aswell as intelligent route planning, are well-known solutions to enhance thefuel efficiency, research has also demonstrated that the adoption offuel-efficient driving behaviors could lead to further savings. In this work,we propose a novel framework to promote fuel-efficient driving behaviorsthrough real-time automatic monitoring and driver feedback. In this framework,a random-forest based classification model developed using historical data toidentifies fuel-inefficient driving behaviors. The classifier considersdriver-dependent parameters such as speed and acceleration/decelerationpattern, as well as environmental parameters such as traffic, road topography,and weather to evaluate the fuel efficiency of one-minute driving events. Whenan inefficient driving action is detected, a fuzzy logic inference system isused to determine what the driver should do to maintain fuel-efficient drivingbehavior. The decided action is then conveyed to the driver via a smartphone ina non-intrusive manner. Using a dataset from a long-distance bus, wedemonstrate that the proposed classification model yields an accuracy of 85.2%while increasing the fuel efficiency up to 16.4%