scholarly journals Sensing with Polarized LIDAR in Degraded Visibility Conditions Due to Fog and Low Clouds

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2510
Author(s):  
Ayala Ronen ◽  
Eyal Agassi ◽  
Ofer Yaron
Keyword(s):  
Autonomous Navigation ◽  
Polarization State ◽  
Poor Performance ◽  
Weather Conditions ◽  
Atmospheric Environment ◽  
Analysis Tool ◽  
Signal Loss ◽  
Expected Performance ◽  
Electro Magnetic ◽  
Lidar Equation

LIDAR (Light Detection and Ranging) sensors are one of the leading technologies that are widely considered for autonomous navigation. However, foggy and cloudy conditions might pose a serious problem for a wide adoption of their use. Polarization is a well-known mechanism often applied to improve sensors’ performance in a dense atmosphere, but is still not commonly applied, to the best of our knowledge, in self-navigated devices. This article explores this issue, both theoretically and experimentally, and focuses on the dependence of the expected performance on the atmospheric interference type. We introduce a model which combines the well-known LIDAR equation with Stocks vectors and the Mueller matrix formulations in order to assess the magnitudes of the true target signal loss as well as the excess signal that arises from the scattering medium radiance, by considering the polarization state of the E–M (Electro-Magnetic) waves. Our analysis shows that using the polarization state may recover some of the poor performance of such systems for autonomous platforms in low visibility conditions, but it depends on the atmospheric medium type. This conclusion is supported by measurements held inside an aerosol chamber within a well-controlled and monitored artificial degraded visibility atmospheric environment. The presented analysis tool can be used for the optimization of design and trade-off analysis of LIDAR systems, which allow us to achieve the best performance for self-navigation in all weather conditions.

ASSESSMENT OF ADVERSE METEOROLOGICAL CONDITIONS FOR DISPERSION OF POLLUTANTS IN THE ATMOSPHERIC AIR OF CHERKASSY CITY

2016 ◽  
Author(s):  
Olga Khrystoslavenko ◽  
Ingrida Chemerys
Keyword(s):  
Pollution Index ◽  
Anthropogenic Pollution ◽  
Weather Conditions ◽  
Multiple Correlation Coefficient ◽  
Atmospheric Environment ◽  
Atmospheric Air ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Reduce Emissions ◽  
The City

Nowadays there is a tendency towards increasing of anthropogenic pollution in the atmospheric air in the large cities. Therefore, important measures have to be taken for the improvement of the atmospheric environment. In order to optimize the quality of air in the city and reduce emissions from stationary and mobile sources, it is important to predict of the state of the atmospheric air of the city, which is based on the analysis of the characteristics of adverse weather conditions conducive to the accumulation of harmful impurity in a lower (ground) layer of air. The paper identifies and analyzes the conditions in the Cherkassy city (Ukraine) for the period of 2011–2015, provides correlation and regression analysis of air pollution index with adverse weather conditions (the multiple correlation coefficient R = 0.55–0.87). The current research shows that the maximum number of days with adverse weather conditions is in autumn (77.20±4,96) and the lowest number is in spring (58.60±4.40), the greatest number of days matching several adverse weather conditions were found in January and October (4.80±0.20 and 4.60±0.24, respectively). Recommendations to reduce the content of harmful impurities in the atmospheric air of the city are suggested.

Detection of Target Persons Using Deep Learning and Training Data Generation for Tsukuba Challenge

2018 ◽  
Vol 30 (4) ◽  
pp. 513-522 ◽  
Author(s):  
Yuichi Konishi ◽  
◽  
Kosuke Shigematsu ◽  
Takashi Tsubouchi ◽  
Akihisa Ohya
Keyword(s):  
Deep Learning ◽  
Autonomous Navigation ◽  
Detection System ◽  
Computational Cost ◽  
Weather Conditions ◽  
Recognition System ◽  
Training Data ◽  
Urban Setting ◽  
Data Generation ◽  
And Training

The Tsukuba Challenge is an open experiment competition held annually since 2007, and wherein the autonomous navigation robots developed by the participants must navigate through an urban setting in which pedestrians and cyclists are present. One of the required tasks in the Tsukuba Challenge from 2013 to 2017 was to search for persons wearing designated clothes within the search area. This is a very difficult task since it is necessary to seek out these persons in an environment that includes regular pedestrians, and wherein the lighting changes easily because of weather conditions. Moreover, the recognition system must have a light computational cost because of the limited performance of the computer that is mounted onto the robot. In this study, we focused on a deep learning method of detecting the target persons in captured images. The developed detection system was expected to achieve high detection performance, even when small-sized input images were used for deep learning. Experiments demonstrated that the proposed system achieved better performance than an existing object detection network. However, because a vast amount of training data is necessary for deep learning, a method of generating training data to be used in the detection of target persons is also discussed in this paper.

scholarly journals Rapid Evaluation of the Effects of Policies Corresponding to Air Quality, Carbon Emissions and Energy Consumption: An Example from Shenzhen, China

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1221
Author(s):  
Shi-Qi Yang ◽  
Jia Xing ◽  
Wen-Ying Chen ◽  
Fen Li ◽  
Yun Zhu
Keyword(s):  
Air Quality ◽  
Carbon Emissions ◽  
Environmental Science ◽  
Assessment System ◽  
Atmospheric Environment ◽  
Surface Model ◽  
Analysis Tool ◽  
So2 Emissions ◽  
Mobile Sources ◽  
Secondary Industry

Efficient environmental policies are necessary in the improvement of air quality and reduction in carbon emissions, and the interactions between policy, activity, emissions, and environment comprise a cycle allowing the evaluation of the effects of implemented policies. Based on the establishment of the connection between environmental parameters and policy context using a quantifiable methodology, in this study, we formulated a rapid and simplified pattern for the evaluation of the effects of policies concerning the atmospheric environment, and applied it to the evaluation and improvement of policies for Carbon dioxide (CO2) reduction and air quality enhancement in the sample city of Shenzhen. The Response Surface Model-Visualization and Analysis Tool (RSM-VAT) in the Air Benefit and Cost and Attainment Assessment System (ABaCAS) was applied as the core tool. The required reductions in Fine particulate matter (PM2.5) and Sulfur dioxide (SO2) emissions for 2014–2019 are expected to be achieved; however, the expected reductions in Nitrogen oxides (NOx) emissions (mainly from road mobile sources) and Volatile organic compounds (VOCs) emissions (mainly from secondary industry and road mobile sources) are less certain. According to the simulated concentration of PM2.5 in 2019, it is necessary to reduce the concentrations of air pollutants, both within and outside Shenzhen. The background weather conditions may be the main reason for the increased concentrations of Ozone (O3) in October compared to those in July. Reductions in NOx and VOCs tend to be the main factors driving changes in O3 concentrations. Policies have been formulated and implemented in a wide array of areas. According to the quantitative comparative analysis of the policies, and the relevant activities, the greatest challenge in reducing NOx and VOCs emissions is presented by the oil-powered vehicles in the road mobile sector and organic solvent production in the secondary industry sector. Therefore, in an effort to achieve better air quality and ensure that CO2 emissions reach a peak in Shenzhen by 2025, we propose key improvements in policies based on interdisciplinary cooperation, involving not only atmospheric and environmental science, but also governance and urban planning.

Development and Testing of an Automated Building Commissioning Analysis Tool (ABCAT)

2010 ◽  
Author(s):  
John D. Bynum ◽  
David E. Claridge ◽  
Jonathan M. Curtin
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Weather Conditions ◽  
The United States ◽  
Commercial Buildings ◽  
Test Case ◽  
Analysis Tool ◽  
Component Level ◽  
Building Commissioning ◽  
Occupant Comfort

Experience has shown that buildings on average may consume 20% more energy than required for occupant comfort which by one estimate leads to $18 billion wasted annually on energy costs in commercial buildings in the United States. Experience and large scale studies of the benefits of commissioning have shown the effectiveness of these services in improving the energy efficiency of commercial buildings. While commissioning services do help reduce energy consumption and improve performance of buildings, the benefits of the commissioning tend to degrade over time. In order to prolong the benefits of commissioning, a prototype fault detection and diagnostic (FDD) tool intended to aid in reducing excess energy consumption known as an Automated Building Commissioning Analysis Tool (ABCAT) has been developed. ABCAT is a first principles based whole building level top down FDD tool which does not require the level of expertise and money often associated with more detailed component level methods. The model based ABCAT tool uses the ASHRAE Simplified Energy Analysis Procedure (SEAP) which requires a smaller number of inputs than more sophisticated simulation methods such as EnergyPlus or DOE-2. ABCAT utilizes a calibrated mathematical model, white box method, to predict energy consumption for given weather conditions. A detailed description of the methodology is presented along with test application results from more than 20 building years worth of retrospective applications and greater than five building years worth of live test case applications. In this testing, the ABCAT tool was used to successfully identify 24 significant energy consumption deviations in five retrospective applications and five significant energy consumption deviations in four live applications.

scholarly journals A review of algorithms and techniques for image-based recognition and inference in mobile robotic systems

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142097227
Author(s):  
Thomas Andzi-Quainoo Tawiah
Keyword(s):  
Autonomous Vehicles ◽  
Action Plan ◽  
Pedestrian Detection ◽  
Sensory Information ◽  
Poor Performance ◽  
Weather Conditions ◽  
False Alarms ◽  
Processing Control ◽  
Covering Design ◽  
Algorithmic Approaches

Autonomous vehicles include driverless, self-driving and robotic cars, and other platforms capable of sensing and interacting with its environment and navigating without human help. On the other hand, semiautonomous vehicles achieve partial realization of autonomy with human intervention, for example, in driver-assisted vehicles. Autonomous vehicles first interact with their surrounding using mounted sensors. Typically, visual sensors are used to acquire images, and computer vision techniques, signal processing, machine learning, and other techniques are applied to acquire, process, and extract information. The control subsystem interprets sensory information to identify appropriate navigation path to its destination and action plan to carry out tasks. Feedbacks are also elicited from the environment to improve upon its behavior. To increase sensing accuracy, autonomous vehicles are equipped with many sensors [light detection and ranging (LiDARs), infrared, sonar, inertial measurement units, etc.], as well as communication subsystem. Autonomous vehicles face several challenges such as unknown environments, blind spots (unseen views), non-line-of-sight scenarios, poor performance of sensors due to weather conditions, sensor errors, false alarms, limited energy, limited computational resources, algorithmic complexity, human–machine communications, size, and weight constraints. To tackle these problems, several algorithmic approaches have been implemented covering design of sensors, processing, control, and navigation. The review seeks to provide up-to-date information on the requirements, algorithms, and main challenges in the use of machine vision–based techniques for navigation and control in autonomous vehicles. An application using land-based vehicle as an Internet of Thing-enabled platform for pedestrian detection and tracking is also presented.

scholarly journals Inquisition on Cost & Time Overrun in Road Construction Projects in Kashmir.

2019 ◽  
Vol 9 (2) ◽  
pp. 2956-2965
Keyword(s):  
Construction Projects ◽  
Research Study ◽  
Road Construction ◽  
Poor Performance ◽  
Weather Conditions ◽  
Modern Technology ◽  
Key Factors ◽  
Jammu And Kashmir ◽  
Design Changes ◽  
Substantial Problem

The success of the project is generally acknowledged by the fact whether the project is completed within the time and budget. There are many challenges in this for completion of project within time and budget, this result in poor performance of project often. The construction cost and time overrun is most substantial problem in Jammu and Kashmir. This problem is faced by all parties like contractors, clients, subcontractors and suppliers. The aim of this research study is to find out factors that leads to cost and time overrun in road construction projects in Jammu and Kashmir. The results of this research shows the key factors that cause cost and time overrun in road construction projects in Jammu and Kashmir are Land acquisition problems, payment delay for completed work, delay in shifting of utilities inclement weather conditions, Security situation, design changes during construction, Lack of modern technology and market inflations

12 Equipping Humans--Optimizing Performance: The Role of Technology in Human Capital

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 142-143
Author(s):  
Jonathon Hoek ◽  
Monty Miller
Keyword(s):  
Human Capital ◽  
Value Chain ◽  
Business Performance ◽  
Poor Performance ◽  
Critical Issue ◽  
Digital Transformation ◽  
Analysis Tool ◽  
Swine Production ◽  
Soft Skill ◽  
Pork Production

Abstract Human capital influences 100% of the production and business performance achieved in swine production. “Five years from now 35% of the of important workforce skills will have changed.” according to Digital Transformation 1. To meet the coming digital transformation in swine production, the need for innovative human capital strategies has never been greater. Boessen, Artz, and Schutlz 2 found labor is a critical issue for the industry. High performing swine farms achieve it because of their people. Swan 3 noted that “pigs do not achieve excellence; people achieve excellence through their pigs.” Agriculture has been slow to adapt soft skill strategies due to the ambiguity in the value proposition. Cost metrics of turnover, poor performance, and safety are traditionally buried within the P&L under labor with labor impacting 100% of the value chain in pig and pork production. The need to analyze the human impact has never been more crucial, and this led to the 2019 Labor intel Study by sponsors and Summit SmartFarms. Schmidt and Hunter‍ 4 found that the use of general mental ability testing improves the predictability and utility of hiring the right person. The increased validity can be as high as 20% vs. traditional means of recruitment. Assessments have proven to provide intelligence on humans for many years through the principles of industrial psychology. Platforms like Cloverleaf and the Organizational Cultural Inventory have harnessed all the attributes of digital transformation to provide human intelligence for predictive and prescriptive human optimization. Pigmanship training has accelerated the value of assessments through precision training. The next step is to integrate these platforms into an analysis tool that combines production, human resource, and assessment data to quantify the value of organizational health.1. Digital Transformation by Thomas Siebel Rosetta Books 2 National Pork Board. Employee Compensation and HR Practices in Pork Production.3 Swan, M.K. Swine Human Resources: Managing Employees. 4 Schmidt FL, Hunter JE. The validity and utility of selection methods in personnel psychology

scholarly journals Entrepreneurial Orientation and Performance of Small and Medium- Sized Enterprises in Nairobi City County, Kenya

2020 ◽  
Vol 2 (3) ◽  
pp. 36-48
Author(s):  
Joshua Kilungu Kivuitu ◽  
Jane Karugu
Keyword(s):  
Economic Growth ◽  
Risk Taking ◽  
Entrepreneurial Orientation ◽  
Business Performance ◽  
Poor Performance ◽  
Weather Conditions ◽  
The Body ◽  
Small Enterprise ◽  
Adverse Weather ◽  
And Performance

Kenya’s economic growth is estimated to have decelerated to 4.4 per cent in the third quarter of 2017 compared to 5.6 per cent in a similar period of 2016. During the quarter, the macroeconomic fundamentals remained largely stable and supportive of growth. However, uncertainty associated with political environment coupled with effects of adverse weather conditions slowed down the performance of the economy. As a result, most sectors of the economy posted poor performance during the quarter under review compared to the same quarter of 2016. (Kenya Economic Outlook, 2017).The Micro and Small Enterprises sector in Kenya is regarded as the driving force to spur economic growth, innovation and job creation. The general objective of the study is to investigate the effect of entrepreneurial orientation on performance of SMES in Nairobi County, Kenya. The specific objectives of the study are; to determine the effect of innovation, analyze the effect of risk taking and establish the effect of pro-activeness on performance of SMEs in Nairobi County. This study used descriptive research design. The study population is 2300 SMEs registered to operate in Nairobi County. Stratified sampling was used to obtain a sample size of 230 respondents. Data was collected using semi-structured questionnaires. Descriptive and inferential statistics was used in the analysis of data using SPSS and Ms Excel. Data was then be presented using tables, graphs and figures. Based on the findings, the study concluded that entrepreneurial orientation is useful as a Predictor of performance of SMEs.  All the Entrepreneurial orientation dimensions: innovativeness, proactiveness and risk-taking had Positive significant effect on performance of SMEs. This implies that behaviors associated with innovativeness, proactiveness and risk-taking when taken as an overall strategic may indeed help SMEs in Kenya to grow. Further, the results suggests that EO-oriented activities within an organization not only results in better performance but also assist owners of SMEs to make better decision regarding the choice of strategic resources acquired. The findings of this study add to our understanding on the relationship between EO and performance of SMEs and represent an important contribution to the body of knowledge in the field of entrepreneurship. Based on the research findings and conclusion this study recommends that: SMEs need to embrace the entrepreneurial orientation dimensions, innovativeness, risk taking and proactiveness to increase business performance. Entrepreneurs need to consider risk-taking to effectively and successfully respond to the dynamic environments that require organizations to increase decision-making speed. Entrepreneurs should be innovative and develop new products ahead of their competitors. They should also be proactive by carrying out strategic environmental scans for new opportunities in the market. Finally, there is need for the Department of Micro and Small-Enterprise Development (DMSED) to consider in its blue print, facilitation of workshops and seminars for small and medium entrepreneurs to sensitize them on the significance of these dimensions in business performance. This study recommends that future researchers should carry out research on the Factors that play a mediating role in the influence of entrepreneurial orientation on performance like munificence, dynamism and hostility should be in future studies.

scholarly journals Curvefusion—A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6918
Author(s):  
Shitong Du ◽  
Helge A. Lauterbach ◽  
Xuyou Li ◽  
Girum G. Demisse ◽  
Dorit Borrmann ◽  
...  
Keyword(s):  
Autonomous Navigation ◽  
Degrees Of Freedom ◽  
Laser Scanner ◽  
Estimation Method ◽  
Poor Performance ◽  
Calibration Method ◽  
Probabilistic Methods ◽  
Estimation Methods ◽  
Novel Approach ◽  
Time Calibration

Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences.

scholarly journals Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles

2017 ◽  
Vol 71 (1) ◽  
pp. 241-256 ◽  
Author(s):  
Riccardo Polvara ◽  
Sanjay Sharma ◽  
Jian Wan ◽  
Andrew Manning ◽  
Robert Sutton
Keyword(s):  
Autonomous Navigation ◽  
Optimal Solution ◽  
Weather Conditions ◽  
Research Area ◽  
Unmanned Surface Vehicles ◽  
Planning Methods ◽  
The Difference ◽  
Optimisation Methods ◽  
Intelligent Methods ◽  
Almost All

The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.

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