A framework for flexible integration in robotics and its applications for calibration and error compensation

Robotics has been considered as a viable automation solution for the aerospace industry to address manufacturing cost. Many of the existing robot systems augmented with guidance from a large volume metrology system have proved to meet the high dimensional accuracy requirements in aero-structure assembly. However, they have been mainly deployed as costly and dedicated systems, which might not be ideal for aerospace manufacturing having low production rate and long cycle time. The work described in this thesis is to provide technical solutions to improve the flexibility and cost-efficiency of such metrology-integrated robot systems. To address the flexibility, a software framework that supports reconfigurable system integration is developed. The framework provides a design methodology to compose distributed software components which can be integrated dynamically at runtime. This provides the potential for the automation devices (robots, metrology, actuators etc.) controlled by these software components to be assembled on demand for various assembly applications. To reduce the cost of deployment, this thesis proposes a two-stage error compensation scheme for industrial robots that requires only intermittent metrology input, thus allowing for one expensive metrology system to be used by a number of robots. Robot calibration is employed in the first stage to reduce the majority of robot inaccuracy then the metrology will correct the residual errors. In this work, a new calibration model for serial robots having a parallelogram linkage is developed that takes into account both geometric errors and joint deflections induced by link masses and weight of the end-effectors. Experiments are conducted to evaluate the two pieces of work presented above. The proposed framework is adopted to create a distributed control system that implements calibration and error compensation for a large industrial robot having a parallelogram linkage. The control system is formed by hot-plugging the control applications of the robot and metrology used together. Experimental results show that the developed error model was able to improve the 3 positional accuracy of the loaded robot from several millimetres to less than one millimetre and reduce half of the time previously required to correct the errors by using only the metrology. The experiments also demonstrate the capability of sharing one metrology system to more than one robot

A Model for Detecting Accounting Frauds by using Machine Learning

This paper aims to develop a machine learning model that enables to predict signs of financial statement frauds by combining the domain knowledge of machine learning and accounting. Inputs of this model is a published dataset of financial statements, and outputs involve the conclusions whether the predicted financial statements indicate the signs of financial statement frauds or not. Currently, XGBoost is recognized as one of the most popular classification methods with fast performance, flexibility, and scalability. However, its default properties are not suitable for fraudulent detecting of imbalanced datasets. To overcome this drawback, this research introduces a new machine learning model based on XGBoost technique, called f(raud)-XGBoost. The proposed model not only inherits XGBoost advantages but also enables it to detect financial statement frauds. We apply the Area Under the Receiver Operating Characteristics Curve and NDCG@k to perform the evaluation process. The experimental results show that the new model performs slightly better than three existing models including logistic regression model that is based on financial ratios, Support-vector-machine model, and RUSBoost mode

A nonlinear concrete damaged plasticity model for simulation reinforced concrete structures using ABAQUS

The reinforced concrete structure is typical and widely used in many fields. The behavior of concrete is nonlinear and complex. Especially, when cracks/crushings occurred in softening phase. Thus, It is important to find a damaged model of concrete with high reliability in the numerical simulation. The nonlinear behavior of concrete is the most feature used in the simulation. This characteristic is expressed through the parameters defining the yield surface, the flow potential, and the nonlinear relationship of stress-strain in the cases of tension and compression. This paper introduces a damaged concrete model that applies to the simulation of reinforced concrete structures. The reinforced concrete beam and flat slab are selected as examples to evaluate the reliability of the model presented. Through the results achieved, the model used in this paper shows high reliability and can be used to simulate more complex reinforced concrete structures

The trilemma of sustainable industrial growth: evidence from a piloting OECD’s Green city

Can green growth policies help protect the environment while keeping the industry growing and infrastructure expanding? The City of Kitakyushu, Japan has actively implemented eco-friendly policies since 1967 and recently inspired the pursuit of sustainable development around the world, especially in the Global South region. However, empirical studies on the effects of green growth policies are still lacking. This study explores the relationship between road infrastructure development and average industrial firm size with air pollution in the city through the Environmental Kuznets Curve (EKC) hypothesis. Auto-Regressive Distributed Lag (ARDL) and Non-linear Auto-Regressive Distributed Lag (NARDL) methods were applied on nearly 50-years’ time series data, from 1967 to 2015. The results show that the shape of the EKC of industrial growth, measured by average firm size, depends on the type of air pollution: inverted N-shaped relationships with NO2 and CO, and the U-shaped relationships with falling dust particle and Ox. Regarding infrastructure development, on the one hand, our analysis shows a positive effect of road construction on alleviating the amount of falling dust and CO concentration. On the other hand, the emissions of NO2 and Ox are shown to rise when plotted against road construction. The decline of CO emission, when plotted against both industrial growth and road development, indicates that the ruthlessness of the local government in pursuing green growth policies has been effective in this case. However, the story is not straightforward when it comes to other air pollutants, which hints at the limits of the current policies. The case of Kitakyushu illustrates the complex dynamics of the interaction among policy, industry, infrastructure, and air pollution. It can serve as an important reference point for other cities in the Global South when policies are formed, and progress is measured in the pursuit of a green economy. Finally, as an OECD SDGs pilot city and the leading Asian green-growth city, policymakers in Kitakyushu city are recommended to revise the data policy to enhance the findability and interoperability of data, as well as to invest in the application of big data

Exploring Environmental Kuznets Curves of Kitakyushu: 50-year Time-series Data of the OECD SDGs Pilot City

Can green growth policies help protect the environment while keeping the industry growing and infrastructure expanding? The City of Kitakyushu, Japan, has actively implemented eco-friendly policies since 1967 and recently inspired the pursuit of sustainable development around the world, especially in the Global South region. However, empirical studies on the effects of green growth policies are still lacking. This study explores the relationship between road infrastructure development and average industrial firm size with air pollution in the city through the Environmental Kuznets Curve (EKC) hypothesis. Auto-Regressive Distributed Lag (ARDL) and Non-linear Auto-Regressive Distributed Lag (NARDL) methods were applied on nearly 50-years’ time series data, from 1967 to 2015. The results show that the shape of the EKC of industrial growth, measured by average firm size, depends on the type of air pollution: inverted N-shaped relationships with NO2 and CO, and the U-shaped relationships with falling dust particle and Ox. Regarding infrastructure development, on the one hand, our analysis shows a positive effect of road construction on alleviating the amount of falling dust and CO concentration. On the other hand, the emissions of NO2 and Ox are shown to rise when plotted against road construction. The decline of CO emission, when plotted against both industrial growth and road development, indicates that the ruthlessness of the local government in pursuing green growth policies is effective in this case. However, the story is not straightforward when it comes to other air pollutants, which hint at limits in the current policies. The case of Kitakyushu illustrates the complex dynamics of the interaction among policy, industry, infrastructure, and air pollution. It can serve as an important reference point for other cities in the Global South when policies are formed, and progress is measured in the pursuit of a green economy. Finally, as an OECD SDGs pilot city and the leading Asian green-growth city, policymakers in Kitakyushu city are recommended to revise the data policy to enhance the findability and interoperability of data as well as to invest in the application of big data

On how religions could accidentally incite lies and violence: Folktales as a cultural transmitter

This research employs the Bayesian network modeling approach, and the Markov chain Monte Carlo technique, to learn about the role of lies and violence in teachings of major religions, using a unique dataset extracted from long-standing Vietnamese folktales. The results indicate that, although lying and violent acts augur negative consequences for those who commit them, their associations with core religious values diverge in the final outcome for the folktale characters. Lying that serves a religious mission of either Confucianism or Taoism (but not Buddhism) brings a positive outcome to a character (βT_and_Lie_O= 2.23; βC_and_Lie_O= 1.47; βT_and_Lie_O= 2.23). A violent act committed to serving Buddhist missions results in a happy ending for the committer (βB_and_Viol_O= 2.55). What is highlighted here is a glaring double standard in the interpretation and practice of the three teachings: the very virtuous outcomes being preached, whether that be compassion and meditation in Buddhism, societal order in Confucianism, or natural harmony in Taoism, appear to accommodate two universal vices—violence in Buddhism and lying in the latter two. These findings contribute to a host of studies aimed at making sense of contradictory human behaviors, adding the role of religious teachings in addition to cognition in belief maintenance and motivated reasoning in discounting counterargument

Level of Determinants Impact on Buyer’s Purchasing Intention in Motor Liability Insurance: Case of Vietnam

Motor liability insurance has been included in the compulsory insurance category that each vehicle owner of every type of motor vehicle must participate in in Vietnam. However, in fact, the participation in this type of insurance is not popular and not strictly managed. This paper presents an approach to modeling and analyzing the possible determinants that may affect the intention to buy motor liability insurance for motor vehicle owners in the North of Vietnam. The target audience of this study is motorcycle owners. Based on the theories of buying intention, buying behavior and the specific characteristics of this insurance, this study has proposed a model with 4 factors influencing intention to participate in the insurance: Attitudes towards risk and insurance, subjective standards, Insurance Perceptions, and Product Accessibility. Taken together, these factors model a consumer's tendency toward insurance intentions for motorbike owners. The results show that all of the above factors have influence on the intention of motorcycle owners to participate in insurance. Keywords: Motor liability insurance, Buying intention, Purchase decision DOI: 10.7176/EJBM/13-8-11 Publication date: April 30th 202

Factors affecting the decision to choose a university of high school students: A study in An Giang Province, Vietnam

It is important to provide high school students with the necessary information for them to consult and make a decision to choose a university. The study aims to identify and evaluate the influence of factors in the decision to choose a university for high school students. The questionnaire survey method was used to collect data from 393 students from eight high schools in An Giang Province, Vietnam. Exploratory factor analysis and linear regression were used to analyze the data. The research results show that students are quite satisfied and quite certain with their decision to choose a university, while there are six important factors affecting the decision to choose a university. Influential factors with decreasing order of magnitude are: i) Factors consulted by teachers, family, friends, and relatives; ii) Factors of future job opportunities; iii) Factors of media activities; iv) Factors of learning conditions; v) Factors of university reputation; vi) Factors belong to the students themselves. The findings of the study show that there is no statistically significant difference between the group of males and females, between grades 10, 11, and 12. Besides, there is a statistically significant difference between students in high schools. The findings of this study have theoretical and practical implications for university admissions in Vietnam. Proposals made to university administrators were discussed. From the research results, we want to help students find the right university, and support universities to improve the efficiency of admissions

The Effect of ENSO on Hydrological Structure and Environment in the South Central Coast – Vietnam

ENSO (El Niño-Southern Oscillation) phenomena have impacted on the hydrodynamic regime and environmental factors of the tropical ocean in general. In case of Vietnamese South-Central Waters, impacts of ENSO only focused on issues of changing seasonal wind, seawater temperature anomalies, changing of water masses as the air-sea interaction. Based on several data sets collecting in the period of 2003-2017, new finding of seawater temperature, salinity and environmental factors was identified in the water masses of Vietnamese South-Central Waters. The highest salinity was 35.4 ‰. During the El Nino event, increasing water temperature and salinity caused to move the deeper water masses to be closer to the sea surface than that initial depth in the neutral period. During the La Nina event, the temperature of most water masses reduced by 0.1-3°C, and then these water masses could be affected to the deeper layer. During the phase from strong ENSO event towards the neutral time, nutrient salts of the 4 water masses were lower concentration in the neutral year, causing the lack of phosphorus in sea surface water masses