Configuration Rules Research Articles

Analyzing Count Min Sketch with Conservative Updates

Count-Min Sketch with Conservative Updates (CMS-CU) is a popular algorithm to approximately count items’ appearances in a data stream. Despite CMS-CU’s widespread adoption, the theoretical analysis of its performance is still wanting because of its inherent difficulty. In this paper, we propose a novel approach to study CMS-CU and derive new upper bounds on both the expected value and the CCDF of the estimation error under an i.i.d. request process. Our formulas can be successfully employed to derive improved estimates for the precision of heavy-hitter detection methods and improved configuration rules for CMS-CU. The bounds are evaluated both on synthetic and real traces.

Optimal capacity configuration of hydro-wind-PV hybrid system and its coordinative operation rules considering the UHV transmission and reservoir operation requirements

Hydropower is utilized to regulate the fluctuations of wind and photovoltaic (PV) power in the hydro-wind-PV renewable energy system (H-RES), which can effectively improve energy utilization. However, it is challenging to determine the optimal capacity configuration considering power delivery and power output characteristics simultaneously. This study launched a research framework for determining the optimal capacity configuration and short-term coordinative operation rules considering the ultra-high voltage (UHV) transmission and reservoir operation requirements. Firstly, a scenario analysis technique was proposed to generate joint wind-PV scenarios considering the uncertainties and correlation. Secondly, a two-stage adaptive complementary operation strategy was presented. Subsequently, a nested mathematical model of capacity configuration and coordinated operation optimization of H-RES was established. Finally, the framework was applied to a case study in Qinghai Province, China. The results show that: (1) it is recommended the H-RES wind-PV penetration rate should not exceed 50% when the system is configured; (2) the proposed model and strategy can eliminate hydropower abrupt fluctuations by 32.7% on average; (3) the power grid role of the H-RES varies by season and it shows great potential of bearing the baseload in summer and autumn. Therefore, this research is instructive in the planning and actual operation of the large-scale H-RES.

Quicsdn: Transitioning from Tcp to Quic for Southbound Communication in Sdns

Transport and security layer protocols make up the backbone of communication between end point devices. In Software Defined Networking (SDN), these protocols play a crucial role in both control-plane and data-plane communications. However, the current transport and security layer protocols: TCP and TLS, are unable to keep up with the pace of SDN application development. For these applications, the TCP/TLS protocol suite generates excessive network overhead. After identifying the main overhead origins, we demonstrate that using QUIC as the SDN transport layer protocol significantly reduces the overhead and improves the efficiency of the network. In this paper, we introduce quicSDN to enable robust, low-overhead communication between the controller and switches. We ran a variety of experiments to highlight quicSDN's benefits, and compared experimental results with transport-layer overhead prediction models. quicSDN's performance is evaluated in terms of network overhead reduction and we also demonstrated quicSDN's connection migration capabilities. First, we compare the differences in controller-switch communication overhead between tcpSDN(SDN over TCP) and quicSDN. Overhead reduction was measured in three scenarios: flow rule installation, queue configuration, and flow rule statistics polling. Second, we compare the connection migration of quicSDN and tcpSDN; QUIC's ability to quickly migrate connections allows for reduced total traffic in comparison to TCP. Overall, our results show that quicSDN outperformed tcpSDN in our SDN scenarios, and as such, QUIC is a promising candidate as an SDN transport layer protocol in the future.

CMOS Implementation of 5T SRAM with Low Power Dissipation

SRAM is a very fast memory with low power consumption. The main objective of this work is to perform a 64-digit SRAM with 90 nm innovation. Execution depended on a granular perspective. SRAM's base module is similar to an N-MOS inverter, flip-flop, and semiconductor. We design this module according to the configuration rule of the ? format. Using Harvard technology, SRAM can easily retrieve information from memory. To create advanced rational circuits, it is important to see how an SRAM is assembled and how it works. The bottom line is that with 0.12 micron 90nm technology, we are developing a 5T SRAM and we can read and write. It is a fundamental part of a computer's central processing unit. RAM is a building block made up of several circuits. The 64-bit SRAM reader was developed with MICROWIND and DSCH2. With the MICROWIND program, the developer can design and simulate an integrated circuit at the physical description level. DSCH2 allows switching of digital logic design.

Efficient data validation for geographical interlocking systems

In this paper, an efficient approach to data validation ofdistributed geographical interlocking systems (IXLs) is presented.In the distributed IXL paradigm, track elements are controlled bylocal computers communicating with other control components overlocal and wide area networks. The overall control logic isdistributed over these track-side computers and remote servercomputers that may even reside in one or more cloud server farms.Redundancy is introduced to ensure fail-safe behaviour,fault-tolerance, and to increase the availability of the overallsystem. To cope with the configuration-related complexity of suchdistributed IXLs, the software is designed according to the digitaltwin paradigm: physical track elements are associated with softwareobjects implementing supervision and control for the element. Theobjects communicate with each other and with high-level IXL controlcomponents in the cloud over logical channels realised bydistributed communication mechanisms. The objective of this articleis to explain how configuration rules for this type of IXLs can bespecified by temporal logic formulae interpreted on Kripke Structurerepresentations of the IXL configuration. Violations ofconfiguration rules can be specified using formulae from awell-defined subset of LTL. By decomposing the completeconfiguration model into sub-models corresponding to routesthrough the model, the LTL model checking problem can be transformedinto a CTL checking problem for which highly efficient algorithmsexist. Specialised rule violation queries that are hard to expressin LTL can be simplified and checked faster by performing sub-modeltransformations adding auxiliary variables to the states of theunderlying Kripke Structures. Further performance enhancements areachieved by checking each sub-model concurrently. The approachpresented here has been implemented in a model checking tool whichis applied by Siemens Mobility for data validation of geographicalIXLs.

Non-Traditional Layout Design for Robotic Mobile Fulfillment System with Multiple Workstations

This paper studies the layout design of a robotic mobile fulfillment system with multiple workstations. This is a parts-to-picker storage system where robots hoist pods and bring them directly to the workstations for stationary pickers to retrieve required items. As few research efforts have focused on determining the optimal locations of workstations in such systems, we develop an integer programming model to determine the location of workstations to minimize the total traveling distance of robots. In addition, we investigate the near-optimal workstation location patterns (i.e., some general workstation configuration rules) in the context of both traditional and flying-V layouts. A series of experiments led to the following findings: (1) the flying-V layout can save 8∼26% of travel distance compared with the traditional layout, and the sacrifice of space use is only 2∼3% for medium or large warehouses; (2) instead of solving the optimization model, the proposed 2n rule and n+1 rule are simple and easily implemented ways to locate workstations, with travel distance gaps of less than 1.5% and 5% for traditional and flying-V layouts, respectively; and (3) the “optimal” cross-aisle angle (i.e., θ) in flying-V layout can be set as large as possible as long as the cross-aisle intersects the left or right edge of the warehouse.

A heuristic configuration solving process planning method for mechanical product configuration by imitating the crystal crystallization process

Configuration design is a process of composing customized product from a set of predefined component types with a set of well-defined rules which decide how items can be selected and combined. The configuration solving process is to instantiate all component types into instances. However, the configurator faces the problem of how to determine the instantiation order of component types in the configuration solving process. An unreasonable instantiation order may reduce the accuracy of the configuration result and extend the time required to finish the configuration. In response to the above problem, a heuristic configuration solving process planning method by imitating the crystal crystallization process (CCP) was proposed. According to the product configuration rules, the frequent itemset mining algorithm was used to mine the correlation between the attribute parameters of the product configuration unit. By imitating the CCP and utilizing the calculated coupling strength, the configurator will determine a suitable instantiation sequence for component types. This method has been applied in the GB10 elevator produced by Canny Elevator. It is found that the configuration accuracy has been improved and the consumption of time has been reduced.

Configurations of business founder resources, strategy, and environment determining new venture performance

ABSTRACT We advance a configurational perspective that assumes a new venture’s performance, operationalized as profitability in this study, is determined by interdependencies among its founder(s)’s resources (i.e., human, social, and financial capital), its strategy, and environmental munificence. Using Rule Ensembles, a semiparametric set-theoretic method, we conduct an abductive investigation using a sample of 853 new ventures. The Rule Ensembles technique identifies five multivariate interdependencies (rules) that enhance new venture profitability and two that reduce it. Indicators of business founder resources are involved in all of the seven model rules. Four configurational rules, including a strategy and/or environmental component, equally emerge.

Construction of One-Dimensional Nonuniform Number Conserving Elementary Cellular Automata Rules

An effort to study one-dimensional nonuniform elementary number conserving cellular automata (NCCA) rules from an exponential order rule space of cellular automata is an excellent computational task. To perform this task effectively, a mathematical heritage under the number of conserving functions over binary strings of length [Formula: see text] has been highlighted along with their number conserving cellular automata rules (either uniform or nonuniform). A basic approach for the construction of some feasible nonuniform NCCA rules of any finite configuration with the assistance of nine uniform elementary CA rules has been investigated. From our construction procedure, recurrence equations are formulated as suitably solved to ascertain the actual range of NCCA rules. The state transition diagrams (STDs) of NCCA rules are analyzed. While classifying the binary strings through STDs, we found a fascinating optical insight that equal weight strings from a class whose cardinality is the same as the binomial coefficient [Formula: see text] where [Formula: see text] is the length and [Formula: see text] is the weight of the binary string.

NORA: An Approach for Transforming Network Management Policies into Automated Planning Problems.

Realizing autonomic management control loops is pivotal for achieving self-driving networks. Some studies have recently evidence the feasibility of using Automated Planning (AP) to carry out these loops. However, in practice, the use of AP is complicated since network administrators, who are non-experts in Artificial Intelligence, need to define network management policies as AP-goals and combine them with the network status and network management tasks to obtain AP-problems. AP planners use these problems to build up autonomic solutions formed by primitive tasks that modify the initial network state to achieve management goals. Although recent approaches have investigated transforming network management policies expressed in specific languages into low-level configuration rules, transforming these policies expressed in natural language into AP-goals and, subsequently, build up AP-based autonomic management loops remains unexplored. This paper introduces a novel approach, called NORA, to automatically generate AP-problems by translating Goal Policies expressed in natural language into AP-goals and combining them with both the network status and the network management tasks. NORA uses Natural Language Processing as the translation technique and templates as the combination technique to avoid network administrators to learn policy languages or AP-notations. We used a dataset containing Goal Policies to evaluate the NORA’s prototype. The results show that NORA achieves high precision and spends a short-time on generating AP-problems, which evinces NORA aids to overcome barriers to using AP in autonomic network management scenarios.

Application of Intelligent Sensing in Power Test System

Smart grid is the development trend of the power industry under the new situation. As an important part of smart grid, smart power consumption directly faces the society and power users, and plays a very important role in the construction of smart grid. With the development of intelligent power construction, the traditional power service can not meet the needs of users to participate in demand side management and interact with the power grid in time. Therefore, the construction of a new intelligent sensing power measurement system to improve the quality and level of intelligent power service has become the focus of current power industry research. Firstly, according to the requirements analysis and design principles of the intelligent sensing power detection system, this paper proposes the function analysis and three-tier architecture design of the intelligent sensing power detection system. The intelligent sensing power detection system is logically divided into business application layer, network communication layer and sensing device layer. Externally, it realizes data integration and interaction with power consumption information collection system, marketing business management system and other intelligent power consumption information systems. Internally, it realizes interconnection with user side perception interactive terminal and perception unit, and realizes data acquisition, information transmission and control functions of power consumption terminal equipment. This paper presents a method of applying intelligent sensing technology to data editing environment of power simulation software, and realizes online error detection of simulation original data, automatic completion of keywords, automatic prompt of data card and intelligent help of data domain. The power simulation data editing software with intelligent sensing function developed in this paper is suitable for primary and advanced users, which can improve work efficiency and reduce error rate. The configurable rule management method applied in the implementation of intelligent sensing technology extends the application of data editor to the developers of models and algorithms, and provides a simple way for the timely upgrade and release of new models and algorithms.

Analysis of Integrated Winding Configuration in Dual-Electrical-Port Machine

A novel winding configuration named “integrated winding” was proposed to realize function of original two sets of windings, i.e. produces two independent-control magnetic fields with different pole-pairs (not slot-harmonics). The concrete realization method is through the coordinated operation of controllers, two current components with different phases are injected into the same coil to generate required magnetic fields according to star of slots. Because the magnetic field produced by the winding is only related to the instantaneous current in each coil, rather than specific connection of coils. The integrated winding not only simplifies the winding configuration, but also increases the electric loading, thus, improving torque density. The general method of integrated winding configuration and current injection rule are established. The split-phase rules of integrated winding are summarized and the feasibility of six-phase and twelve-phase integrated windings is then proved through finite element analysis (FEA). A prototype is fabricated and the experimental results verify the availability of the integrated winding.

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

In recent years, technological paradigms such as Internet of Things (IoT) and machine learning have become very important due to the benefit that their application represents in various areas of knowledge. It is interesting to note that implementing these two technologies promotes more and better automatic control systems that adjust to each user’s particular preferences in the home automation area. This work presents Smart Home Control, an intelligent platform that offers fully customized automatic control schemes for a home’s domotic devices by obtaining residents’ behavior patterns and applying machine learning to the records of state changes of each device connected to the platform. The platform uses machine learning algorithm C4.5 and the Weka API to identify the behavior patterns necessary to build home devices’ configuration rules. Besides, an experimental case study that validates the platform’s effectiveness is presented, where behavior patterns of smart homes residents were identified according to the IoT devices usage history. The discovery of behavior patterns is essential to improve the automatic configuration schemes of personalization according to the residents’ history of device use.

GalaxyCloudRunner: enhancing scalable computing for Galaxy.

The existence of more than 100 public Galaxy servers with service quotas is indicative of the need for an increased availability of compute resources for Galaxy to use. The GalaxyCloudRunner enables a Galaxy server to easily expand its available compute capacity by sending user jobs to cloud resources. User jobs are routed to the acquired resources based on a set of configurable rules and the resources can be dynamically acquired from any of four popular cloud providers (AWS, Azure, GCP or OpenStack) in an automated fashion. GalaxyCloudRunner is implemented in Python and leverages Docker containers. The source code is MIT licensed and available at https://github.com/cloudve/galaxycloudrunner. The documentation is available at http://gcr.cloudve.org/.

THE METHOD OF HYBRID INDOOR NAVIGATION MODEL AUTOMATIC CONFIGURATION

The proposed method of automatic configuration of the hybrid model of navigation of people and mobile robots inside the premises allows creating a unified semantic description of contextual information, parameters of navigation methods, as well as configuration rules and calibration of these methods using the navigation ontology and rules system.

A Rule-Based System for Quality Control in Brake Disc Production Lines

Currently, challenges in quality improvement have driven various enterprises to create quality management systems in smart factories. The development of quality management systems enables quality control for reviewing product quality, identification, and eliminating product failures. However, process adjustment in quality control decisions may be hard to determine when failures are detected. To overcome this problem, an expert system (ES) that applies the failure mode and effects analysis (FMEA) method for developing quality control systems in brake disc production lines is considered. This quality control system concentrates on analyzing product defects that occur frequently in the production line and will lead to an improved performance of the braking system; the selected product defects are disc thickness variation (DTV), runout, and parallelism. This quality control system developed two modules, the designed FMEA (DFMEA) and component FMEA, which apply a rule-based algorithm for selecting actions. We propose the rules of configuration into the expert system code. The results indicate that the operator can carry out a quality control system with decision-making that can be supported by intelligent searching and reasoning in an expert system.

A Decade of Air Quality in Bogotá: A Descriptive Analysis

In this work we apply a rigorous and reproducible data analytics process for validation and analysis of the historical data from Bogota (Colombia) air quality monitoring network since 1998. The reasons for addressing this research study stem from the lack of a consistent approach for cleaning, validating and reporting air quality data. By analyzing the whole dataset, we are aiming at providing citizens and the city authorities with a clear view of the current situation of air quality and of its historical evolution. Without any loss of generality, we focus our analysis on both respirable and fine particulate matter (PM10 and PM2.5) concentrations, which in Bogota and worldwide are source of concern for their negative impacts on human health. We develop a reproducible and flexible data cleaning methodology for particulate matter concentration data reported by the local authorities, which allows customizing and applying configurable validation rules. Then, we present statistical descriptive analyses by providing intuitive data visualizations, characterizing historical and spatial change of air pollutant levels. Results raise concerns for the high percentage of invalid data, as well as the high levels of PM2.5 and PM10 ambient concentrations as observed in the valid portion of the available data, which frequently exceed national and international air quality standards. The data exhibit encouraging signs of air quality improvement, particularly for PM10. However, the analyses indicate that significant differences exist across Bogota, and particularly in the south-west zone of the city annual concentrations of particulate matter are up to three or four times the WHO recommendations. We are confident on the methodology and results from our analysis are useful both for local environmental authority and the general public to help in obtaining consistent conclusions from the available data.

RES: Real-Time Video Stream Analytics Using Edge Enhanced Clouds

With increasing availability and use of Internet of Things (IoT) devices such as sensors and video cameras, large amounts of streaming data is now being produced at high velocity. Applications which require low latency response such as video surveillance, augmented reality and autonomous vehicles demand a swift and efficient analysis of this data. Existing approaches employ cloud infrastructure to store and perform machine learning-based analytics on this data. This centralized approach has limited ability to support real-time analysis of large-scale streaming data due to network bandwidth and latency constraints between data source and cloud. We propose RealEdgeStream (RES) an edge enhanced stream analytics system for large-scale, high performance data analytics. The proposed approach investigates the problem of video stream analytics by proposing (i) filtration and (ii) identification phases. The filtration phase reduces the amount of data by filtering low-value stream objects using configurable rules. The identification phase uses deep learning inference to perform analytics on the streams of interest. The phases consist of stages which are mapped onto available in-transit and cloud resources using a placement algorithm to satisfy the Quality of Service (QoS) constraints identified by a user. We demonstrate that for a 10K element data streams, with a frame rate of 15–100 per second, the job completion in the proposed system takes 49 percent less time and saves 99 percent bandwidth compared to a centralized cloud-only based approach.

MODEL-BASED PRODUCT CONFIGURATION OF HIGH VARIETY PRODUCT PORTFOLIOS

AbstractFacing a rising competitive pressure, manufactures create advantages when they are able to offer customer-specific products to the conditions of a mass production article. Traditional configurators support the creation of personalized products from the elements of a modular product system, but are based on a pre-defined set of rules. The model based approach changes the environment of configuration from static configuration rules to the dependencies defined within the product's system model. So, by regarding target quantities of the user, the configurator identifies the optimal variant.

A Novel Imperialist Competitive Algorithm for Scheme Configuration Rules Mining of Product Service System

When designing a product service system scheme, the most critical issue is how to quickly and accurately translate customer value requirements into scheme technical characteristics. It is difficult to solve this problem by only the experience and knowledge of engineers. Effective configuration rule is necessary to assist engineers in scheme design. Therefore, a method of scheme configuration rules mining of product service system based on objective data rather than subjective data is proposed, and a new imperialist competition algorithm is introduced as an alternative method to solve multi-objective optimization problems. On the basis of the traditional imperialist competition algorithm, the empire initialization is carried out by adopting the progressive Latin hypercube sampling method, which improves the uneven distribution of the empire in the search space and expands the search range of the algorithm. This algorithm introduces imperialism splitting mechanism into the revolution process, so that it can quickly jump out of the local optimum and effectively avoid premature convergence. Besides, it introduces clonal evolution operator in competition process to reproduce the countries with strong influence. The clonal populations are randomly crossed and high-efficiency mutated to guide the algorithm forward in the optimal solution direction. Furthermore, the support–confidence framework is extended to form a rule evaluation system consisting of support, confidence, interest, comprehensible and Kulczynski metric. The rule evaluation system significantly improves the quality of rules and obtains more meaningful rules. Finally, the proposed method is used to mine the data in the historical case database of NC machine tool enterprises. The method has fast solving speed and can obtain high-quality rules.