Performance evaluation of laser guided leveler

The study was conducted at Bangladesh Agricultural Research Institute (BARI) farm on clay loam soil during Rabi season of 2010-2011. The treatments consisted of laser land leveling (T1) and control (non-leveled) (T2). A preliminary field survey was done using staff gage. Initially a base station was established to dispense laser ray uniformly. The laser ray erected from base station guided the sensor of the stuff gage and the leveler. Elevation data was collected from the different points of the field and made an average. The maximum gage reading were 247.0 cm and the minimum gage reading was 219.2 cm. Average gage readings of the laser leveled plot was 235.66 cm that was settled for auto adjustment. Therefore, huge amount of soils (16.46 cm high) was cut from the highest point and subsequently had to fill to the low points. Finally, an equal gage reading of 235.66 cm was observed after leveling the plot. The laser leveler (Leica MLS700) was used hitching with a TAFE tractor. The field was leveled with manual control initially and finally it was operated with auto adjustment. Two operators, 25 litter diesels and total 6 hours time were required during this leveling. Wheat was cultivated in leveled land (T1) and non-leveled land (T2). Laser leveling was insured for improvement in nutrient use efficiencies, option for precision farming, reduces weed problems, and improves uniformity of crop maturity. There was better distribution of water in leveled plot, which helped to reduce irrigation application time 1 hour. Due to uniformity of moisture content improved germination and crop establishment was found which reflected in higher plant population (239 m-2). Maximum yield (3.41 t ha-1) was obtained in T1 due to longer panicle (10.89 cm), more grain per plant (27.47) and 1000 grain weight (47.38 g) compared to yield of T2 (2.62 t ha-1). DOI: http://dx.doi.org/10.3329/ijarit.v4i2.22655 Int. J. Agril. Res. Innov. & Tech. 4 (2): 82-86, December, 201

Analysis of nonlinear dynamics of fully submerged payload hanging from offshore crane vessel

The nonlinear dynamic responses of a fully submerged payload hanging from a fixed crane vessel are investigated numerically. A three dimensional fully nonlinear time domain model based on the boundary element method is implemented to perform the analysis. Both the payload and fixed crane vessel are considered to be periodically excited by regular waves inside the numerical tank. The motion of the payload is found to exhibit various nonlinear phenomena (for example, sub-harmonic motion, period doubling behavior) due to the presence of fixed crane vessel. Analysis tools such as the phase trajectory, bifurcation diagram and Poincaré map are used to investigate the motion characteristics of this submerged payload which is undergoing constrained pendulum motions in various scenarios. Parametric studies are also performed by varying several design parameters in order to evaluate the sensitivity of the nonlinear phenomena. Different orientations of the crane vessel and submerged payload are also considered and the results obtained reveal several important conclusions concerning the dynamic behavior of the submerged payload of offshore crane vessel during operations. It is found that change of wave motion frequency coupled with various orientations of the floating barge and submerged payload significantly alters the payload motion behavior and introduces various nonlinear phenomena. The present study can be further extended to identify the limits of the operating conditions of floating cranes and to devise techniques to control or damp the unexpected motions of the submerged payload

A review of internet of energy based building energy management systems: Issues and recommendations

© 2013 IEEE. A building energy management system (BEMS) is a sophisticated method used for monitoring and controlling a building's energy requirements. A number of potential studies were conducted in nearly or net zero energy buildings (nZEBs) for the optimization of building energy consumption through efficient and sustainable ways. Moreover, policy makers are approving measures to improve building energy efficiency in order to foster sustainable energy usages. However, the intelligence of existing BEMSs or nZEBs is inadequate, because of the static set points for heating, cooling, and lighting, the complexity of large amounts of BEMS data, data loss, and network problems. To solve these issues, a BEMS or nZEB solution based on the Internet of energy (IoE) provides disruptive opportunities for revolutionizing sustainable building energy management. This paper presents a critical review of the potential of an IoE-based BEMS for enhancing the performance of future generation building energy utilization. The detailed studies of the IoE architecture, typical nZEB configuration, different generations of nZEB, and smart building energy systems for future BEMS are investigated. The operations, advantages, and limitations of the existing BEMSs or nZEBs are illustrated. A comprehensive review of the different types of IoE-based BEMS technologies, such as energy routers, storage systems and materials, renewable sources, and plug-and-play interfaces, is then presented. The rigorous review indicates that existing BEMSs require advanced controllers integrated with IoE-based technologies for sustainable building energy usage. The main objective of this review is to highlight several issues and challenges of the conventional controllers and IoE applications of BEMSs or nZEBs. Accordingly, the review provides several suggestions for the research and development of the advanced optimized controller and IoE of future BEMSs. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the development of the future BEMS applications

A case study of transport of tropical marine boundary layer and lower tropospheric air masses to the northern midlatitude upper troposphere

Low-ozone (<20 ppbv) air masses were observed in the upper troposphere in northern midlatitudes over the eastern United States and the North Atlantic Ocean on several occasions in October 1997 during the NASA Subsonic Assessment, Ozone and Nitrogen Oxide Experiment (SONEX) mission. Three cases of low-ozone air masses were shown to have originated in the tropical Pacific marine boundary layer or lower troposphere and advected poleward along a warm conveyor belt during a synoptic-scale disturbance. The tropopause was elevated in the region with the low-ozone air mass. Stratospheric intrusions accompanied the disturbances. On the basis of storm track and stratospheric intrusion climatologies, such events appear to be more frequent from September through March than the rest of the year. Copyright 2000 by the American Geophysical Union

Techno-Economic Analysis and Environmental Impact of Electric Buses

Electric vehicles are a leading candidate in the clean energy market. This paper aims to analyse the feasibility of the deployment of electric buses (EB) based on the existing bus routes in Brunei, by the use of life cycle cost analysis and the analysis of the parameters that influence the overall life cycle cost. The findings from the study revealed that EB are significantly more expensive than diesel buses (DB), with their acquisition and maintenance costs contributing substantially to their overall life cycle cost. In order to promote EB deployment, the government needs to look simultaneously into providing subsidies for EB and imposing taxes on DB, the provision of charging infrastructure, and ensuring maintenance capability, as well as increasing the current subsidised diesel price. It was also shown that increasing the cost of diesel to the average US diesel price of USD$3.101/L, an initial subsidy of USD$67,586 towards the purchase of EB, and a tax of USD$67,586 for the purchase of DB would allow EB to compete in the market, with the amount of tax and subsidy being gradually reducible over time, as EB and battery technology becomes more mature. From an environmental perspective, the emissions from EB come out higher than the emissions from DB. The efficiency of electric power generation needs to be enhanced, and renewable energy sources and the adoption of carbon capture technology need to be explored in order to exploit the full benefit of EB and ensure more View Full-Tex

Intelligent Control Algorithm for Energy Management System of Light Electric Vehicles

A state-based logic control algorithm was developed to coordinate a multi-source energy management system (EMS) for light electric vehicles (LEVs), such as scooters. This work was undertaken in view of the increasing importance of hybrid electric vehicles (HEVs) in many rapidly developing Asian countries. The multiple energy sources in this investigation were batteries, fuel cells (FC) and super-capacitors (SCs). Since each resource has its own advantages and disadvantages, a combination of the resources provides a more reliable and powerful energy model for hybrid electric vehicles (HEV).An algorithm was developed to manage the switching of the multiple energy resources efficiently. The performance of the proposed model in terms of vehicle acceleration and load power was measured against the ECE-47 test drive cycle. The sources of energy changeover were examined at 50% of thebattery state of charge (SOC) or under heavy load conditions. The results showed a close match of the model to the test cycle under both normal and heavy load cycle conditions. The feasibility of the proposed intelligent controlling algorithm for the EMSof light electric vehicles was thus verified. This study could contribute huge benefit to the manufacturers and research institutions involved in lightelectric vehicle

GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning

Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to alleviate this issue and to better leverage textual knowledge for tasks, this study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. We discover that existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. To overcome these issues, we propose GIMLET, which unifies language models for both graph and text data. By adopting generalized position embedding, our model is extended to encode both graph structures and instruction text without additional graph encoding modules. GIMLET also decouples encoding of the graph from tasks instructions in the attention mechanism, enhancing the generalization of graph features across novel tasks. We construct a dataset consisting of more than two thousand molecule tasks with corresponding instructions derived from task descriptions. We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks. Experimental results demonstrate that GIMLET significantly outperforms molecule-text baselines in instruction-based zero-shot learning, even achieving closed results to supervised GNN models on tasks such as toxcast and muv

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques.

State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions