Stand-alone Environment Research Articles

Impact Assessment of Petrol Filling Stations on Groundwater Quality in Akure Metropolis, Ondo State

Abstract: The presence of PFSs in urban areas has increased significantly, leading to conflicts with existing land use planning and violations of the minimum environmental safety requirements set by the Department of Petroleum Resources (DPR) and have adverse social and environmental impacts despite their socioeconimic importance. This paper aim to investigate the spatial distribution of petrol filling stations and their impact on groundwater quality in Akure Metropolis of Ondo State. A total of 147 PFSs and 140 water wells were identified and recorded. Laboratory analysis was done purposefully on wells near PFSs that were both 26 years or older and still functional, as older underground storage tanks (USTs) pose a potential threat to groundwater safety. Findings revealed 119 functional filling stations, 28% located within residential area and 68% domiciled in commercial areas, 2% in outskirt of town and 1% in standalone environment. Findings showed all mapped filling stations were found to be within 50m from built up (residential) area, 104 mapped stations within residential and commercial areas violates the DPR standard. Using spatial analysis, it was also found that 116 of the 147 (78.9%) mapped filling stations were within 400m away from the next. 108 of the 147 (73.47%) mapped PFSs violated the standard of distance not less than 15meters from the edge of the road to the nearest pump. The findings also showed that groundwater system is contaminated by PAHs, which comprises; naphthalene.F, pyrene and acenaphthylene.F. Low mean concentrations of PAHs recorded in this study suggest that there is no major compromise in the quality of groundwater due to the operation of PFSs in Akure metropolise Hand-dug Wells in the study area. It is recommended that spatial compliance for siting PFSs should be strictly adhered to in order to avoid disaster that may occur due to the hazard they pose

The new GeoModel suite, a lightweight detector description and visualization toolkit for HEP

The GeoModel toolkit is an open-source suite of standalone tools that provides the user with lightweight tools to describe, visualize, test, and debug detector descriptions and geometries for HEP standalone studies and experiments. GeoModel has been designed with independence and responsiveness in mind and offers a development environment free of other large HEP tools and frameworks, and with a very quick development cycle. With very few and lightweight dependencies, GeoModel is easy to install on all systems, in a modular way; and pre-compiled binaries are provided for the major platforms, for a quick and easy installation. Coded entirely in C++, GeoModel offers the user tools to describe geometries inside C++ code or in external XML files, create persistent representation with a low disk footprint and interactively visualize and inspect the geometry in a 3D view. It also offers a plugin mechanism and an optional Geant4 application to simulate the described geometry in a standalone environment. GeoModel has been developed as part of the software for the ATLAS experiment at the LHC, and evolved towards an experiment-independent toolkit. In this contribution, we describe all the available tools, with a focus on the latest additions, which provide users with more visualization, debug, and simulation tools.

IoT-Oriented Distributed Intrusion Detection Methods Using Intelligent Classification Algorithms in Spark

With the rapid development and wide application of the 5G mobile communication and the explosive security threats of the Internet of things (IoT), distributed intrusion detection is one of the hot topics in the intrusion detection field of network security. The classification algorithm is a kind of the most representative and classical algorithms of artificial intelligence (AI), and it is an important technique for intrusion detection in order to distinguish the attack traffic from massive network data. In order to solve the problem to detect massive and complex network attack traffic in IoT, in this study, we propose the distributed intrusion detection framework and method using intelligent classification algorithms in Spark. We first introduce several mainstream classification algorithms provided by Spark. Second, the distributed intrusion detection procedure using intelligent classification algorithms is given. Next, the overall framework of the proposed model is built. Finally, a series of comparison experiments by the binary classification and quintuple classification in six evaluation indicators (i.e., recall, precision, F1-score, FNR, FPR, and ROC curve) indicate that the naive Bayes has a worse classification performance than that of other classification algorithms, and the classification effect in a cluster environment is almost the same as that in a stand-alone environment.

Efficient Uncertain Sequence Pattern Mining Based on Hadoop Platform

In the Internet of Things (IoT) era, information is collected by sensor devices, resulting in data loss or uncertain data and other consequences. We need to represent the uncertain data collected using probabilities to extract the useful information for production and application from a huge indeterminate data warehouse. The data in the database has a particular order in time or space, so the High-Utility Probability Sequential Pattern Mining (HUPSPM) has become a new investigation and analysis topic in data processing. After the progress of timestamp, many efficient algorithms for sequential mining have been developed. However, these algorithms have a limitation: they can only be executed in a stand-alone environment and are only suitable for small datasets. Therefore, introducing an advanced graph framework for processing large datasets addresses the shortcomings of the existing methods. The proposed algorithm can avoid repeated database searching, splitting the database, and improve the parallel computing capability. The initial database is pruned according to the existing pruning strategy to effectively reduce the number of candidate sets effectively. Experiments show that the algorithm presented in this paper has excellent advantages in mining high-utility probability sequences in large datasets.

Internet of Things: Towards a Solid Ecosystem of Interconnected Things

The Internet of Things (IoT) is a technological revolution that has changed everything we do and given us a new perspective on our daily lives, but despite the fact that numerous publications have focused on characterizing the many edges and technologies that make up an IoT system, the IoT ecosystem is still seen as too complex to be recognized as a stand-alone environment due to its significant diversity; hence, the objective of this research is to address such a complex environment in a way that highlights its components and distinguishes them both individually and in relation to their broader context. Therefore, the definition of IoT and its emergence were discussed and organized around the timeline of Internet development phases demonstrating that IoT has been a need that has accompanied the presence of the Internet since its early stages, and then its growth and impact were discussed and highlighted with estimates and numbers. On the technical side, each of the following groups, IoT components, protocols, and architectures, was defined, discussed, and grouped in such a way that their intergroup organization, as well as their placement and contribution to the overall ecosystem, was highlighted. This, in addition to the various examples mentioned throughout the discussion, will provide the reader with a better understanding of the Internet of Things and how deeply it has become entwined in our daily lives and routines as a result of its numerous applications.

Research on SVM Remote Sensing Image Classification Based on Parallelization

With the development of technology, the feature dimension and data volume of remote sensing image classification have increased rapidly. However, when remote sensing image classification based on support vector machine (SVM) is used for large-scale data calculation, there are significant limitations in training time. This paper focuses on the parallel processing method of support vector machine (SVM). Based on the popular hybrid parallel support vector machine, a hybrid parallel support vector machine based on sample cross combination is proposed, and carried on the simulation experiment analysis in the stand-alone environment.

Automatic Floorplanning and Standalone Generation of Bitstream-Level IP Cores

Partially reconfigurable designs on field-programmable gate array (FPGA) bring an opportunity for developers to license third-party intellectual property (IP) cores. There are multiple IP licensing models that can be used by the FPGA IP market. Their focus is mainly on feasibility and security; however, two major challenges have been ignored by almost all of them. First, both academic or industrial tools do not provide a flow to generate IPs in a standalone environment. Second, these tools only offer manual floorplanning of the IPs, which is both time and performance inefficient. In this work, we present a framework, that can be used by multiple parties to generate different parts of a design independently, that are compatible with each other. It also provides automatic floorplanning based on mixed-integer linear programming (MILP) that considers the distribution of heterogeneous resources in modern FPGAs, with efficient resource utilization as the main objective. The proposed floorplanning is evaluated with benchmarks from the related work. Furthermore, a use case of internal and open-source designs is used for the validation and evaluation of the independent IP generation and the floorplanner.

Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic, Wind Turbine, and Battery Systems

Renewable energy sources (RESs) are considered to be reliable and green electric power generation sources. Photovoltaics (PVs) and wind turbines (WTs) are used to provide electricity in remote areas. Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment. The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution. This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization (TLBO) named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer’s load at minimal total annual cost (TAC). The reliability of the system is considered by a maximum allowable loss of power supply probability (LPSP max ) concept. The results obtained from the JLBO algorithm are compared with the original Jaya, TLBO, and genetic algorithms. The JLBO results show superior performance in terms of TAC, and the PV–WT–battery hybrid system is found to be the most economical scenario. This system provides a cost-effective solution for all proposed LPSP max values as compared with PV–battery and WT–battery systems.

TACMA: total annual cost minimization algorithm for optimal sizing of hybrid energy systems

In a stand-alone environment, a system comprising of non-renewable source, renewable energy sources (RESs), and energy storage systems like fuel cells (FCs) provide an effective and reliable solution to fulfill the user’s load. In this paper, a diesel generator (DG), photovoltaics (PVs), wind turbines (WTs) and FCs are modeled, optimally sized, and compared in three scenarios: PV–WT–FC–DG, PV–FC–DG, and WT–FC–DG in terms of environmental emission and total annual cost (TAC) for a home, located in Hawksbay, Pakistan. The optimal size of hybrid RESs and their components is achieved using a novel TAC minimization algorithm (TACMA). The TACMA achieves superior results in terms of TAC when it is compared to two algorithm-specific parameter-less schemes: Jaya and teaching learning-based optimization. Further, the PV–WT–FC–DG and PV–FC–DG hybrid systems are found as the most economical and nature-friendly scenarios, respectively.

Test environment for verification of multi-processor interrupt system with virtualization support

Interrupt system is an important part of microprocessors. Interrupts are widely used for interaction with hardware and responding to stimuli. Modern microprocessor interrupt systems include hardware support of virtualization. Hardware support helps to increase the performance of virtual machines. However, including additional functionality may lead to potential errors. The paper presents an overview of approaches used for multi-core microprocessors interrupt system with virtualization support verification. Some definitions and characteristics of interrupt systems that needed to be taken into account in the process of verification are described. Stand-alone verification environment general scheme is presented. Universal Verification Methodology was applied to construct test system. To simplify development of checking module discrete-event with time accounting reference model was used. Sequences of primary requests and automatically generated secondary requests in the special modules named auto-handlers were used for test system behavior randomization. We describe some difficulties discovered in the verification process and corresponding solving methods. Generalized test algorithm stages are presented. Some other techniques for checking the correctness of interrupt system have been reviewed. In conclusion, we provide the case study of applying the suggested approaches for interrupt system verification of microprocessors with “Elbrus” and “SPARC-V9” architectures developed by MCST. The results and further plan of the test system development are presented.

An efficient method for meteorological nephogram recognition in cloud environment

Nephogram could provide important information for meteorological business, and nephogram recognition is a kind of challenge in the meteorological industry. In this paper, a selective neural network ensemble method based on K-means and Hadoop processing technology is proposed. This method combines the neural network, K-means clustering and AdaBoost in cloud environment. The experimental results show that the recognition precision of the method proposed by this paper is higher than that of traditional method in a stand-alone environment.

Enhanced Evolutionary Sizing Algorithms for Optimal Sizing of a Stand-Alone PV-WT-Battery Hybrid System

An increase in the world’s population results in high energy demand, which is mostly fulfilled by consuming fossil fuels (FFs). By nature, FFs are scarce, depleted, and non-eco-friendly. Renewable energy sources (RESs) photovoltaics (PVs) and wind turbines (WTs) are emerging alternatives to the FFs. The integration of an energy storage system with these sources provides promising and economical results to satisfy the user’s load in a stand-alone environment. Due to the intermittent nature of RESs, their optimal sizing is a vital challenge when considering cost and reliability parameters. In this paper, three meta-heuristic algorithms: teaching-learning based optimization (TLBO), enhanced differential evolution (EDE), and the salp swarm algorithm (SSA), along with two hybrid schemes (TLBO + EDE and TLBO + SSA) called enhanced evolutionary sizing algorithms (EESAs) are proposed for solving the unit sizing problem of hybrid RESs in a stand-alone environment. The objective of this work is to minimize the user’s total annual cost (TAC). The reliability is considered via the maximum allowable loss of power supply probability ( L P S P m a x ) concept. The simulation results reveal that EESAs provide better results in terms of TAC minimization as compared to other algorithms at four L P S P m a x values of 0%, 0.5%, 1%, and 3%, respectively, for a PV-WT-battery hybrid system. Further, the PV-WT-battery hybrid system is found as the most economical scenario when it is compared to PV-battery and WT-battery systems.

An approach for deployment of BRS in software-defined network

In the recent years due to technological development, Internet is omnipresent. With advancement, more and more people are connected through Internet, and there has been a paradigm shift in the usage of computer applications. Applications have been migrated from local servers to cloud environment. This led to massive requirement of network connectivity. Traditional networks in most cases are unable to sustain the load, and hence, a new programmable network is required. Software-defined network (SDN) has evolved as a promising solution in the present scenario. However, a number of issues are present as of now in SDN including controller failure, in stand-alone or cluster controller environment, controller placement problem, security issues related to controller, and nodes connected in a subnet or network in a predefined topology. In this paper, a comprehensive solution has been proposed using a backup and restoration system (BRS) that can handle failure of stand-alone, multiple, and distributed controllers. Further, authentication technique has also been incorporated to enhance the security for sustainability.

An Integrative User-Level Customized Modeling and Simulation Environment for Smart Manufacturing

As smart factories are emerging, the importance of modeling and simulation (M&S) continues to increase in the production system. As a result, various commercial tools and environments are provided for production simulation, and manufacturing companies are also applying them to establish a smart manufacturing system. This is used in various ways, such as optimal layout design, scheduling, and fault diagnosis using the acquired smart manufacturing model. However, these model constructions are generally done through a stand-alone environment in which the work type or user level is not considered. It is necessary to use different environments depending on the user level or to rely on M&S experts. Therefore, this paper eliminates this inefficiency and proposes an integrative user-level customized smart manufacturing M&S environment for all users in the production system. It provides three-phase modeling environments appropriate for the user level, including an automatic model synthesis interface and a production line generator. Using this environment, anyone can easily make capacity and logistic models, and simulate them.

The Improved SIFT Algorithm Based on Rectangular Operator and Its Parallel Implementation

The SIFT algorithm is one of the most widely used algorithm which bases on local feature extraction. But it could not meet the requirement of the real-time application due to the high time complexity and low execution efficiency. In order to improve these drawback, the authors optimized the SIFT algorithm by using the Gaussian convolution scale of adaptive scale space. The authors also provided the executive process of the improved SIFT algorithm on the MapReduce programming model and compared its performance in terms of the stand-alone and cluster environment. The experiment result showed that compared to the traditional algorithm, the improved algorithm had high execution efficiency, good speedup, scalability and is suitable for massive amounts of image data processing.

Distributed gene clinical decision support system based on cloud computing

BackgroundThe clinical decision support system can effectively break the limitations of doctors’ knowledge and reduce the possibility of misdiagnosis to enhance health care. The traditional genetic data storage and analysis methods based on stand-alone environment are hard to meet the computational requirements with the rapid genetic data growth for the limited scalability.MethodsIn this paper, we propose a distributed gene clinical decision support system, which is named GCDSS. And a prototype is implemented based on cloud computing technology. At the same time, we present CloudBWA which is a novel distributed read mapping algorithm leveraging batch processing strategy to map reads on Apache Spark.ResultsExperiments show that the distributed gene clinical decision support system GCDSS and the distributed read mapping algorithm CloudBWA have outstanding performance and excellent scalability. Compared with state-of-the-art distributed algorithms, CloudBWA achieves up to 2.63 times speedup over SparkBWA. Compared with stand-alone algorithms, CloudBWA with 16 cores achieves up to 11.59 times speedup over BWA-MEM with 1 core.ConclusionsGCDSS is a distributed gene clinical decision support system based on cloud computing techniques. In particular, we incorporated a distributed genetic data analysis pipeline framework in the proposed GCDSS system. To boost the data processing of GCDSS, we propose CloudBWA, which is a novel distributed read mapping algorithm to leverage batch processing technique in mapping stage using Apache Spark platform.

Method of Constructing Point Generalization Constraints Based on the Cloud Platform

As an important part of spatial data, the point feature has always been an essential element in web maps and navigation maps. With the development of location-based services and the rapid increase in volunteered geographic information and social media data, the amount of point data is increasing day by day, resulting in inevitable problems of overlay and congestion during visualization. Map generalization provides multiple algorithms that can be used to select, aggregate and make typification of points or point clusters. For the generalization of point data, however, the traditional stand-alone computing environment has difficulty with real-time realization. Currently, the rapid development of cloud computing technology provides a powerful support for improving the efficiency of map generalization. However, compared with the stand-alone environment, the data decomposition and the real-time display of point generalization in the cloud platform imposes higher requirements on the point generalization constraints, which play an important role in point-generalized process control. Based on the computational characteristics of the cloud platform, this paper analyzes the changes in point generalization constraints. In addition, our work proposes the constraints of point generalization based on the cloud platform and its construction method, builds a prototype system based on the Hadoop cloud platform. Our prototype system is tested using typical experimental data. Its efficiency and the quality of its results is examined. The results show that the efficiency and quality of point selection can be significantly improved by controlling the point generalization process with the generalization constraints in the cloud computing environment proposed in this paper. This paper provides a possible way for the realization of map generalization in the cloud computing environment. Its usability with real data and with many users accessing it will be the focus of further research.

Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture

The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario, the improvement in the stand-alone environment is limited when considering large-scale mass vector data. Nevertheless, recent parallel dissolved vector buffer algorithms suffer from scalability problems, leaving room for further optimization. At present, the prevailing in-memory cluster-computing framework—Spark—provides promising efficiency for computing-intensive analysis; however, it has seldom been researched for buffer analysis. On this basis, we propose a cluster-computing-oriented parallel dissolved vector buffer generating algorithm, called the HPBM, that contains a Hilbert-space-filling-curve-based data partition method, a data skew and cross-boundary objects processing strategy, and a depth-given tree-like merging method. Experiments are conducted in both stand-alone and cluster environments using real-world vector data that include points and roads. Compared with some existing parallel buffer algorithms, as well as various popular GIS software, the HPBM achieves a performance gain of more than 50%.

Design and implementation of employee management system software (A case study of Eco Bank of Nigeria Bauchi Branch Bauchi State)

This Research report includes a development presentation of an Employee system for managing the staff data within a small company or organization. The work addresses limitations identified with manual method of handling records of employees in a firm by providing a better platform to eliminate fraud, corruption, file hiding and misplacement, records falsification, duplication, fragmentation, inconsistencies and other vices attendant with manual method of handling employee records. Employee management system software is a user friendly package that gives one the fit to accurately monitor employees’ records effortless. it consists of functionally related application program and database, The choice of the programming tools is individual the goal of this Research was to design and develop an employee management system this system was developed to fill existing gaps in the electronic management of employees. This software is designed for stand-alone windows environment, but has the ability to be networked. The interface is PHP with the structured query language (SQL). The implementation of the system will provide speedy retrieval of data as well as enhancing effective and efficient data.

Abstract B1-06: Tumor genotyping using full cloud based pipeline

Abstract With emergence of Next Generation Sequencer (NGS), data processing using cloud computing and parallel computation becomes necessary. To process huge NGS data, especially yielded from illumina Hiseq2000 sequencer, we have developed a Hadoop/Spark based mapping soft “Volt” which used spaced hash index and Smith-Waterman algorithm with comparable speed and accuracy to existing NGS mapping software. This software can run both on standalone environment and Hadoop/Spark environment, and in later case, the software is directory integrated using Hadoop API and can read and write data to Hadoop Distributed File System (HDFS), and mapping process is distributed using MapReduce framework. To speedup mapping speed, the software uses two seeds match for near perfect hit, and uses full length seed hit for mismatched hit. Also, we have developed tumor genotyper “karkinos” which detects single nucleotide variation (SNV), copy number variation (CNV), calculate tumor cellularity simultaneously. In this genotyper, we proposed “Open genotyper format”, by using that, each heuristic filtering algorithm can be implemented by add on style development. We integrated software into one analysis pipeline and tested on large cohort of liver cancer data. Detailed algorithm and performance data will be shown at the meeting. Citation Format: Hiroki Ueda, Shogo Yamamoto, Hiroyuki Aburatani. Tumor genotyping using full cloud based pipeline. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-06.