14th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2011)

The first edition of the Advanced Computing and Analysis Techniques in Physics Research (ACAT) workshop series was held at Fermilab in the year 2000, which was in fact the 7th meeting of its parent the original AIHENP workshop series that started in 1990 in Lyon, France. ACAT 2017 was the 18th workshop in the series, which is growing stronger and increasingly successful. It covered the areas of computing in physics research, computations in theoretical physics, and data analysis algorithms and tools. In this summary article, I will give some historical perspective on the workshop series and provide highlights of this workshop. Since Artificial Intelligence (AI) was the featured theme, I will briefly discuss machine learning (ML) and comment on the latest developments in ML applications in physics research covered at this workshop.

In smart grid, more data will be collected at finer granularity. It will promote the development of power system control and innovation technology. With the advantages of cloud computing technology are becoming increasingly prominent, it is gradually entering into power industry research. This paper focuses on cloud-based data processing and analysis techniques of user side in smart grid. To prove that the integration of cloud computing, smart grid, data analysis technique is practical, we introduced the cloud platform architecture designed for data processing and its key technologies first. Then, main sections of cloud-based data processing, including data collection, storage, analysis and visualization are discussed. Especially, based on smart grid scenes, data analysis methods in data analysis section are described in detail.

Chapter 6 - Naturalistic Driving Studies and Data Coding and Analysis Techniques

In this methodological paper, the authors propose a tool that brings together various quantitative and qualitative data analysis (i.e., mixed analysis) techniques into one meta-framework to assist mixed researchers (who use qualitative and quantitative approaches within the same study) in the data analysis phase of mixed research studies. A meta-framework for mixed analysis techniques is described, which incorporates 13 criteria that methodologists have used to create their mixed analysis typologies. In particular, a heuristic example is used with the aid of screenshots to illustrate how one can utilize several of these data analysis techniques to conduct mixed analyses.

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

Research work is one of the most difficult types of independent work of applicants for the second level of higher education (master's students), the implementation of which requires special knowledge and skills. For master's cadets, preparing and writing a master's thesis is the first experience of research activity. In the Kherson State Maritime Academy, for master's cadets specializing in «Navigation and Management of Sea Vessels», the course «Modern Methods of Scientific Research and Data Processing» (MMSRDP) is provided, which, according to the curriculum and educational and professional program, is included in the cycle of professional training. The program of the academic discipline «Modern Methods of Scientific Research and Data Processing» is drawn up in accordance with the Code of Training, Certification and Watchkeeping for Seafarers STCW-78, IMO-MODEL COURSE 7.01, 7.03. and according to EPP. The direction of our research is to improve the teaching of the course «Modern Methods of Scientific Research and Data Processing» in the Kherson State Maritime Academy to achieve more significant standards of professional competence of applicants for the second level of higher education (master's students) in the specialization «Navigation and Management of Marine Vessels» of KhSMA. This article examines two of the most important components of the MMSRDP course. The first is the «starting» knowledge of the methodology and methods of scientific research, namely, the most significant classical paradigms that determine the essence of the concepts of methodology and methods of scientific research, concepts and issues of modern methods of empirical and theoretical research. The second, no less important component of the MMSRDP course is the study by master's students of specific methods and techniques of data processing and analysis. Our further research is aimed at a more detailed consideration of the second component of the MMSRDP course - the study by master's students of specific methods and techniques of data processing and analysis in the field of technical sciences, in particular, in the field of navigation, which are most necessary for master's students of the navigation specialty, as well as the development of a set of individual tasks for master's students aimed at the application of specific methods and techniques of data collection and analysis

This research was conducted to obtain motives and the usage patterns instagram among the students unimed the economics faculty of economic education course of study. Population in this research is 490 people. Sample a total of 49 people in this research. A theory that used in this research is a theory uses and gratification. Methods used in this research is quantitative research methods. The technique of analysis in this research is a technique of the data analysis by doing tabulation of the results of such data that has been filled by respondents, the data obtained next shown in the form table a frequency distribution. The result of this research revealed that motive respondents using instagram is to “pleasure seeking/ entertainment“ Using patterns of instagram in respondents tend to irregular, according to the research using patterns of instagram generally seen from the frequency of a total of more than 4 ( four ), duration less than 1 ( one ) hours, the use of more at night, content most widely seen is a photo sites tutorial video. Keyword : Motives, Patterns and Instagram

9. Experimental Methods in Rock Magnetism and Paleomagnetism

Sensory-instrumental correlations by combining data analysis and neural network techniques

The Photovoltaics for Utility Scale Applications (PVUSA) project tests two types of PV systems at the main test site in Davis, California: new module technologies fielded as 20-kW Emerging Module Technology (EMT) arrays and more mature technologies fielded as 70- to 500-kW turnkey Utility-Scale (US) systems. PVUSA members have also installed systems in their service areas. Designed appropriately, data acquisition systems (DASs) can be a convenient and reliable means of assessing system performance, value, and health. Improperly designed, they can be complicated, difficult to use and maintain, and provide data of questionable validity. This report documents PVUSA PV system instrumentation and data analysis techniques and lessons learned. The report is intended to assist utility engineers, PV system designers, and project managers in establishing an objective, then, through a logical series of topics, facilitate selection and design of a DAS to meet the objective. Report sections include Performance Reporting Objectives (including operational versus research DAS), Recommended Measurements, Measurement Techniques, Calibration Issues, and Data Processing and Analysis Techniques. Conclusions and recommendations based on the several years of operation and performance monitoring are offered. This report is one in a series of 1994--1995 PVUSA reports documenting PVUSA lessons learned at the demonstration sites in Davis and Kerman, California. Other topical reports address: five-year assessment of EMTs; validation of the Kerman 500-kW grid support PV plant benefits; construction and safety experience in installing and operating PV systems; balance-of-system design and costs; procurement, acceptance, and rating practices for PV power plants; experience with power conditioning units and power quality.

Energy supply is characterized by its diversity, including traditional energy, such as fossil fuels, nuclear power, as well as renewable energy, such as solar, hydroelectric, geothermal, biomass, and wind energy. It involves a complex network system composed of energy generation, energy transformation, energy transportation, and energy consumption. The network does provide the great flexibility for energy transformation and transportation; meanwhile, it presents a complex task for conducting agile energy dispatching when extreme events have caused local energy shortages that need to be restored timely. One of the useful methodologies to solve such a problem is data mining and analysis. Their main objective is to take advantage of inherent tolerance of the imprecision and uncertainty to obtain tractability, robustness, and low solution-cost. The applications and developments of data mining and analysis have amazingly evolved in the last two decades. Many of these applications can be found in the field of renewable energy and energy efficiency where data mining and analysis techniques are showing a great potential to solve the problems that arise in this area. In this chapter, data mining and analysis techniques are briefly introduced. Then the implementation procedures are presented to demonstrate the application of curve fitting for renewable energy network design and optimization, which has the capability to handle the restoration during extreme and emergency situations with uncertain parameters.

Corrupt behavior in Indonesia has existed since the kingdom era. Therefore, a strategy is needed to strengthen ethics and integrity to prevent corruption in Indonesia. The government has also enacted various laws and regulations that serve as guidelines for strengthening the ethics and integrity of government officials to prevent corruption. One is the Law of the Republic of Indonesia Number 28 of 1999 concerning the Administration of a State that is clean and free from corruption, collusion, and nepotism. The research method used is the descriptive qualitative method. The data analysis technique used was the Content Analysis Technique. Nvivo 12 Plus analysis technique uses Crosstab and Sociogram features in data analysis. The Nvivo 12 plus analysis technique is data processing based on the big data obtained. The results of the study show that the strategy that can be used in strengthening ethics and integrity to prevent corruption in Indonesia can be carried out by the government by implementing anti-corruption education in every institution and element of society, which is manifested in various programs and can also be carried out through social media, namely by uploading various acts of corruption. Events around the community can be immediately followed up by law enforcement agencies and through public solid oversight, codes of conduct, and the rule of law.

Nowadays a lot of massive data is stored and typically, the data itself contains a lot of non-trivial but useful information. Data mining techniques can be used to discover this information which can help the companies for decision-making. However, in real life applications, data is massive and is stored over distributed sites. One of my major research topics is to protect privacy over this kind of data. Previously, the important characteristics, issues and challenges related to management of the large amount of data has been explored. Various open source data analytics frameworks that deal with large amount of Data analytics workloads have been discussed. Comparative study between the given frameworks and suitability of the same has been proposed. Digital universe is flooded with huge amount of data generated by number of users worldwide. These data are of diverse in nature, come from various sources and in many forms. To keep with the desire to store and analyze ever larger volumes of complex data, relational databases vendors have delivered specialized analytical platforms that come in many shapes and sizes from software only to analytical services that run in third party hosted environments. In addition new technologies have emerged to address exploding volumes of complex data, including web traffic, social media content and machine generated data including sensor data, global positioning system data.Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data has become a prominent research field, especially when it comes to decision making and data analysis. However, Big data due to its various properties like volume, velocity, variety, variability, value and complexity put forward many challenges. Since Big data is a recent upcoming technology in the market which can bring huge benefits to the business organizations, it becomes necessary that various challenges and issues associated in bringing and adapting to this technology are brought into light. Another challenge is that data collection may not have enough accuracy which will lead to a non-consistent analysis that can critically affect the decision based on this analysis. Moreover, it is clearly apparent that organizations need to employ data-driven decision making to gain competitive advantage. Processing, integrating and interacting with more data should make it better data, providing both more panoramic and more granular views to aid strategic decision making. This is made possible via Big Data exploiting affordable and usable Computational and Storage Resources. Many offerings are based on the Map-Reduce and Hadoop paradigms and most focus solely on the analytical side. Nonetheless, in many respects it remains unclear what Big Data actually is; current offerings appear as isolated silos that are difficult to integrate and/or make it difficult to better utilize existing data and systems. Since data is growing at a huge speed making it difficult to handle such large amount of data (Exabyte). The main difficulty in handling such large amount of data is because that the volume is increasing rapidly in comparison to the computing resources. The Big data term which is being used now a days is kind of misnomer as it points out only the size of the data not putting too much of attention to its other existing properties.If data is to be used to make accurate decisions in time it becomes necessary that it should be available in accurate, complete and timely manner. This makes the data management and governance process bit complex adding the necessity to make Data open and make it available to government agencies in standardized manner with standardized APIs, metadata and formats thus leading to better decision making, business intelligence and productivity improvements.This paper presents a discussion and evaluation for the most prominent techniques used in the processes of data collection and analysis in order to identify the privacy defects in them that affects the accuracy of big data. Depending on the results of this analysis, recommendations were provided for improving data collection and analysis techniques that will help to avoid if not all then most of the problems facing the use of big data in decision making. Keywords: Big Data, Big Data Challenges, Big Data Accuracy, Big Data Collection, Big Data Analytics.

Over the course of time, machine learning has improved the data analysis technique such as face detection and recognition. Many machine learning researches have been implemented in medical treatments. This concept is proposed is inspired from different aspect of hair scalp and other factors. Spatial-temporal data is very useful in weather forecasting and satellite image analysis this technique is implemented to capture necessary data from hair follicle images. Hair is also a subject of human body. There are many factors which can be used to determine health of hair. All this factor including spatial-temporal images, gender, age and hair style is used to predict health of hair. This paper present machine learning algorithm for analysis of medical data for determining health of hair. We use the SVM (support-vector machines) model classifier for analysis of data, after that we get values such as short, straight, wavy and curly.In this paper J48 Algorithms were used to obtain an accurate result compared with other algorithms.J48 with bagging is creating different decision trees for same data that why it is given more accurate results, J48 algorithms will split continuous values through using threshold. This paper 1066 samples were tested using cross validation technique, according to the test, it is found 87.14 % was a correctly classified and 12.85 % was incorrectly classifier. So at the end we get a real time performance is 89.5 %. This paper proves the compatible between hair style and Age-Gender.

There has been increased interest of late on the application of nonlinear methods to economic and financial data due to their robustness in handling large and complex data. With increasingly complex 'big data', focus has shifted into use of robust techniques in analysis of data. Various nonlinear approaches have so far been established including support vector machine which is widely adapted in classification and regression problems. This research project applied support vector regression technique and neural network models in modeling and forecasting economic growth for the five countries in the East Africa Community including Kenya, Uganda, United Republic of Tanzania, Rwanda and Burundi. Data for the period 1990 to 2014 from World Bank databases was used for the research. Support vector model and neural network models were trained using the data for the 1990-2002 whereas the remaining data was used for prediction performance to determine the robustness of the two models on external datasets. The study revealed that specific country models had better performance compared to the combined model and that although the two models compared similarly under specific-country models, the neural network performed better in most countries. The study recommends the use of the two machine learning techniques in economic growth modeling. It also recommends that the performance be compared with the traditional econometric models but using countries with more data periods.