Input Data Flow Research Articles

ПРИМЕНЕНИЕ ТРАНСПОРТНОЙ ЗАДАЧИ ДЛЯ БАЛАНСИРОВКИ НАГРУЗКИ В УСЛОВИЯХ НЕЧЕТКОСТИ ИСХОДНЫХ ДАННЫХ

The structure of the ISO/OSI model does not allow choosing the optimal packet transmission route at the channel level and preventing the formation of ring routes. These functions are performed by the network layer. In general, the problem of packet routing is solved by the switch based on the routing algorithm, which contains a hidden "flooding" mechanism. A packet from the sender's switch is sent to all ports, except for the port where the packet was received. When a packet arrives, the switch analyzes the header and if the address in the header matches the address to which the switch belongs, then the packet is accepted. This situation, combined with the unevenness of sending a message, creates an increased load on switching devices at random times and determines the problem of distributing the input data flow in conditions of pulsating traffic. Pulsating traffic can be considered as a fuzziness lying within certain boundaries. To smooth traffic, it is possible to use clusters of switching devices, which in turn are considered as outgoing devices for the next level of clusters. Thus, to distribute the load, it is possible to apply the flow distribution algorithm used in solving the transport problem.

Mixed noise-guided mutual constraint framework for unsupervised anomaly detection in smart industries

Large-scale sensor and data acquisition systems, integrated with deep learning methodologies, play a pivotal role in enhancing the sustainability and security of smart city environments, exemplifying the critical significance of anomaly detection techniques. Anomaly detection in complex industrial scenarios presents various challenges, such as intricate working environments, limited anomaly samples, and lack of a priori information. Unsupervised anomaly detection based on knowledge distillation enables anomaly detection using only normal samples. However, the similarity in structure between teacher and student models, along with identical input data flow, hampers accurate anomaly detection and localization. To address these issues, we propose MNMC, an unsupervised anomaly detection model consisting of a mixed noise generation module emulating real defects, a mutual constraint module, and an anomaly segmentation module. Firstly, to enhance the student network’s ability to learn robust features, we construct a hybrid noise model comprising dead-leaves noise and perlin noise. This generates features with structural texture and distributional characteristics closer to real anomalies. Secondly, we design a mutual constraint framework to further improve the learning ability of the student network for normal features by constraining representations containing only a single noise. Lastly, for the detection of anomalies at different scales, we propose a new evaluation metric based on equal importance of normal and anomalous regions. Through ablation experiments, we demonstrate the effectiveness of the simulated real defect generation module and the mutual constraints module. Performance experiments on the MVTec dataset show that our method achieves competitive results compared to the current state-of-the-art anomaly detection methods.

The CNN: The Architecture Behind Artificial Intelligence Development

The convolutional neural network (CNN) is a multilayer network architecture that is capable of training itself using an advanced algorithm to produce increasingly accurate results. The CNN is especially effective in spatial image recognition and is used in a multitude of fields, such as image recognition in the medical industry, and image segmentation in the security field. The ability of the CNN to show impressive results is seen in its multilayer composition.
 This multilayer network consists of the convolutional layer, the pooling layer, the activation layer, and the fully connected layer. Here, the convolutional layer, the pooling layer, and the activation layer have their own parameters which will influence the flow of input data throughout the CNN until it produces an ultimate output in the connected layer. As such, this paper will delve into the individual characteristics of each layer, and introduce its relationship with not only its immediate surrounding layers but with the entirety of the CNN. Additionally, this paper will stress important modules and parameters that can help improve the layers.

Insecurity Refactoring: Automated Injection of Vulnerabilities in Source Code

Insecurity Refactoring is a change to the internal structure of software to inject a vulnerability without changing the observable behavior in a normal use case scenario. An implementation of Insecurity Refactoring is formally explained to inject vulnerabilities in source code projects by using static code analysis. It creates learning examples with source code patterns from known vulnerabilities.Insecurity Refactoring is achieved by creating an Adversary Controlled Input Dataflow tree based on a Code Property Graph. The tree is used to find possible injection paths. Transformation of the possible injection paths allows to inject vulnerabilities. Insertion of data flow patterns introduces different code patterns from related Common Vulnerabilities and Exposures (CVE) reports. The approach is evaluated on 307 open source projects. Additionally, insecurity-refactored projects are deployed in virtual machines to be used as learning examples. Different static code analysis tools, dynamic tools and manual inspections are used with modified projects to confirm the presence of vulnerabilities.The results show that in 8.1% of the open source projects it is possible to inject vulnerabilities. Different inspected code patterns from CVE reports can be inserted using corresponding data flow patterns. Furthermore the results reveal that the injected vulnerabilities are useful for a small sample size of attendees (n=16). Insecurity Refactoring is useful to automatically generate learning examples to improve software security training. It uses real projects as base whereas the injected vulnerabilities stem from real CVE reports. This makes the injected vulnerabilities unique and realistic.

ПОРІВНЯЛЬНИЙ АНАЛІЗ ЕФЕКТИВНОСТІ СХЕМ ВИЯВЛЕННЯ ПЕРЕВАНТАЖЕННЯ ТЕЛЕКОМУНІКАЦІЙНОЇ МЕРЕЖІ

The scheme of congestion detection and regulation of input data flow based on the analysis of the sensitivity function of the telecommunication network performance is considered. The gradient of the sensitivity function characterizes the rate of change of this function and provides the optimal direction for adjusting the speed of the data source. To determine the sensitivity function, the use of a simple neural network model of a dynamic system is proposed. Determination of the gradient on the current value of the sign of the sensitivity function of the performance indicator is based on the algorithm of additive increase / multiple decrease. This algorithm is an alternative to the system of overload prediction and flow control, based on the control of the current value of the queue in comparison with a given threshold. The neural model for multi-step prediction of the queue state on the side of the telecommunication network receiver is considered. The results of comparative analysis of congestion control methods based on queue length analysis and sensitivity analysis with 1-step and 3-step horizons predicting network status are presented. The study was conducted for sinusoidal function of the narrow queue. It is shown that the key performance indicators for the sensitivity function-based scheme are better than for the queue length analysis scheme. The queue size-based scheme is more sensitive to changes in queue maintenance speed, and data source speed fluctuations are less sensitive for the sensitivity-based scheme. For systems based on sensitivity function analysis, a 3-step horizon predictor provides better performance and a smaller maintenance queue than a 1-step horizon scheme.

Risk Management of Implementation of Reagentless Treatment of Acid Mine Water

The task of managing the risk of introducing reagentless treatment of acid mine water is considered. The task is solved, first of all, for natural technological complexes formed by coal and copper-zinc-pyrite mines transferred to "wet" preservation due to unprofitability. To solve the problem, a mobile technological complex operating in a flow-through mode is used. The solution of the problem is based on a system decomposition of the object, tasks and research criteria. The object of the analysis are large volumes of heterogeneous flows of mine water arriving for treatment. The set task is a multi-criteria one. Multi-agent modeling integrating several applications interacting by means of input and output data flows is used for the system design. Decomposition of the set task on the basis of a joint study of mine water properties and treatment technology on a laboratory bench, on which it is possible to reproduce the minimum required set of treatment operations and water conditioning is performed. The structure of the system integrating the concept of safety and the concept of extraction of high value products from mine water is offered. Results of X-ray phase analysis of sediments obtained after cavitation treatment of mine water are presented.

Environmental Profile Study of Ozone Decolorization of Reactive Dyed Cotton Textiles by Utilizing Life Cycle Assessment

Research approaches on the use of ecotechnologies like ozone assisted processes for the decolorization of textiles are being explored as against the conventional alkaline reductive process for the color stripping of the cotton textiles. The evaluation of these ecotechnologies must be performed to assess the environmental impacts. Partial “gate to gate” Life Cycle Assessment (LCA) was implemented to study the ozone based decolorization process of the reactive dyed cotton textiles. Experiments were performed to determine input and output data flows for decolorization treatment of reactive dyed cotton textile using the ozonation process. The functional unit was defined as “treatment of 40 g of reactive dyed cotton fabric to achieve more than 94% color stripping”. Generic and specific data bases were also used to determine flows, and International Life Cycle Data system (ILCD) method was selected to convert all flows into environmental impacts. The impact category “Water resource depletion” is the highest for all the ozonation processes as it has the greatest relative value after normalization amongst all the impact indicators. Electricity and Oxygen formation were found to be the major contributors to the environmental impacts. New experimental conditions have been studied to optimize the impacts.

Analysis of Sales System Implementation in Primary Cooperative Tribuana II

Primer KoperasiTribuana II is a business entity (cooperative) under the auspices of Group 2 Kopassus, Kartasura, Sukoharjo. This cooperative was established to meet the needs of members, both from an economic, social and cultural perspective. Primer KoperasiTribuana II was founded on September 14, 1968 under the name Primkopad H-09. This cooperative has changed its name several times, and the last name uses the Primary KoperasiTribuana II. In an effort to meet the needs of the members, the Tribuana II Primary Cooperative carries out several activities, including savings and loans and sales of daily necessities (shops). In fulfilling the needs, especially for stores, the Tribuana II Primary Cooperative sells goods which in its activities are still using the old method (manual). This is considered very ineffective and efficient and can not increase sales profit in a relatively fast time. For that we need a system that can help smooth the sale of goods. In solving this problem, the authors designed a sales information system. In this design, the author uses the Visual FoxPro programming language. The method in this research is waterfal method. The method in this research is through interviews, observation and literature study. The system design is made with context diagrams, HIPO, input output design and Data Flow Design. The system is created in a program with the Visual FoxPro programming language with a database format using MySQL. The result of this design is to produce an application program that will be used for the web-based sales process at the Tribuana II Primary Cooperative and it is hoped that it will facilitate daily transaction activities and compile reports to be faster and more efficient.

Automated expert support complex based on a machine learning semantic processor

The article is devoted to the creation of an intelligent software complex of a self-learning automated expert system for registering, accounting and executing incidents based on artificial intelligence. The creation of the software complex includes the development of an advisory support expert system based on a semantic processor for speech recognition and synthesis, which allows implementing the first line advisory support based on artificial intelligence. The obtained expert system consists of the following blocks: an input data flow module–GSM (voice address); a semantic processor–the system core in which, using special designed algorithms, the input data flow is converted into the language understood by the system for subsequent intelligent processing set by the specified skills, based on self-learning algorithms; a semantic processor language module with a protocol of recognition, analysis and extraction of input and output data context; an incident registration module with the accounting system in the Redmine platform; PostgreSQL skills database; a manual learning module and an output data flow module. The developed expert support digital ecosystem will allow to completely automate the receipt, processing, registration, accounting and decision-making of incoming requests. The complex implementation can replace first line support call centers and will have a significant economic effect on retail, banking, and consulting systems.

Optimization of vehicle speed forecasting horizont on the intercity highway

The movement of the car in the traffic on intercity routes was investigated. Traffic should be energy efficient, safe and comply with the desired schedule. A method for analyzing the input data flow based on a simulation model has been developed. The proposed simulation algorithm is based on the use of available information resources for driving a car. Traffic control involves choosing a speed with known road and traffic restrictions. The presented algorithm allows to consider the expediency of each of speed increase opportunities over the forecast horizon. The content of the algorithm is the optimal redistribution of time resources. Indicators of control quality are absolute deviations from the optimal energy-saving program of free movement and from the planned schedule. The movement of a freight road train on the long-distance highway E−371 was performed. It was found that the total amount of information increases with increasing distance of scanned traffic. However, the share of reliable information is reduced. It was found that the dependence of the quality of vehicle traffic control on the size of the forecast horizon is piecewise-continuous. The dependence has an extreme value of the horizon in each continuous section, at which the deviation from the optimal program is minimal. The obtained results can be applied in modern intelligent transport systems. The research results make it possible to develop and adhere to optimal long-term traffic programs on highways. It solves the problem of managing large data streams. Large amounts of information for forecasting can be submitted in parts with reasonable frequency using the developed methodology

ON SOME PROPERTIES OF TIMED FINITE STATE MACHINES

Sequential reactive systems are formal models of programs that interact with the environment by receiving inputs and producing corresponding outputs. Such formal models are widely used in software engineering, computational linguistics, telecommunication, etc. In real life, the behavior of a reactive system depends not only on the flow of input data, but also on the time the input data arrive and the delays that occur when generating responses. To capture these aspects, a timed finite state machine (TFSM) is used as a formal model of a real-time sequential reactive system. However, in most of known previous works, this model was considered in simplified semantics: the responses in the output stream, regardless of their timestamps, follow in the same order in which the corresponding inputs are delivered to the machine. This simplification makes the model easier to analyze and manipulate, but it misses many important aspects of real-time computation. In this paper we study a refined semantics of TFSMs and show how to represent it by means of Labelled Transition Systems. This opens up a possibility to apply traditional formal methods for verifying more subtle properties of real-time reactive behavior which were previously ignored.

VWA: Hardware Efficient Vectorwise Accelerator for Convolutional Neural Network

Hardware accelerators for convolution neural networks (CNNs) enable real-time applications of artificial intelligence technology. However, most of the existing designs suffer from low hardware utilization or high area cost due to complex data flow. This paper proposes a hardware efficient vectorwise CNN accelerator that adopts a 3 × 3 filter optimized systolic array using 1-D broadcast data flow to generate partial sum. This enables easy reconfiguration for different kinds of kernels with interleaved input or elementwise input data flow. This simple and regular data flow results in low area cost while attains high hardware utilization. The presented design achieves 99%, 97%, 93.7%, and 94% hardware utilization for VGG-16, ResNet-34, GoogLeNet, and Mobilenet, respectively. Hardware implementation with TSMC 40nm technology takes 266.9K NAND gate count and 191KB SRAM to support 168GOPS throughput while consumes only 154.98mW when running at 500MHz operating frequency, which has superior area and power efficiency than other designs.

РОЗПОДІЛ РЕСУРСІВ БАГАТОФАЗНОЇ СИСТЕМИ ОБРОБКИ ВЕЛИКИХ ДАНИХ ПРИ ВИСОКОІНТЕНСИВНОМУ ВХІДНОМУ ПОТОЦІ

Завдання оптимального проектування багатофазної системи обробки великих даних (БФСОВД) при високоінтенсивному вхідному потоці потребують застосування сучасних математичних і комп'ютерних методів та засобів. Предметом дослідження є ресурси багатофазної системи обробки великих даних. Мета дослідження – розроблення методу оптимального розподілу ресурсів БФСОВД при високоінтенсивному вхідному потоці, котрий враховує як можливість проведення аналізу даних у режимі реального часу, так і особливостей високоінтенсивного потоку вхідних даних із багатьох джерел. Результати. Визначено особливості високоінтенсивних потоків вхідних даних та показана можливість апроксимації його з достатньою точністю гауссівським розподілом. Також визначені особливості функціонування багатофазних систем та запропонована схема функціонування багатофазної системи обробки великих даних. Базуючись на отриманих результатах розроблена математична модель процесу оптимізаціі розподілу ресурсів багатофазної системи обробки великих даних при високоінтенсивному вхідному потоці. Для цього використано метод динамічного програмування. Висновок. В результаті отримана мінімальна кількість каналів в кожній фазі БФСОВД, при якій середній час аналізу даних в системі буде мінімальним.

Ensuring the Availability of Services of the Corporate Intellectual Space by Controlling the Flow of Input Data

Ensuring the Availability of Services of the Corporate Intellectual Space by Controlling the Flow of Input Data

PENGGUNAAN METODE LINEAR REGRESSION UNTUK PREDIKSI PENJUALAN SMARTPHONE

Planning and analyzing market needs precisely and efficiently if managed optimally is needed to achieve company success. In practice, existing transaction data used as a reference in planning and analyzing market needs. This company needs a tool to predict future sales. This information is needed because a good sales prediction will help understand what items must be distributed according to market needs so that companies can reduce uncertainty in decision making. The purpose of this study is to create an information system can do smartphone sales forecasting at 82 Cell Mayang with the Linear Regression method. The sales forecasting system is using the Linear Regression method, The goal to create a sales forecasting system by determining the sales volume with a certain period by looking at the cost of advertising and the number of sales. The research method used includes observation, interviews and literature studies. Designing using DAD includes entity relation diagrams, context diagrams, the hierarchy of input process output and data flow diagrams. The programming language used is Visual Basic.Net and the sql server 2008 database. Features in the sales forecasting application include processing data items, customer data, incoming product data, sales data, and forecasting data. The test results show the MAPE value is 0.032 and the MSE value is 5.16. From this value, it can be said that the prediction of smartphone sales with the Linear Regression method on 82 Cell Mayang is categorized as very good. Whereas for the blackbox testing that has been carried out, it shows that the smartphone sales forecasting system in 82 Cell Mayang, Sukoharjo has been going well.Keywords: forecasting, sales prediction, incoming product data, linear regression, visual basic

Background Information Detection in Substations Based on Anomaly Detection

The substations are important parts of modern electrical grids. In this sense, it is necessary to detect the anomaly and problems in it. In this paper, we study on the information detection in substations based on traditional anomaly detection algorithms. The data flow from the background information is represented by feature vectors. And those from the historical data are used to build the background references. Afterward, the feature vector of the input data flow is examined using the anomaly detection algorithm. Based on the results, the anomaly in the background information in the substations can be found and located. Then, some high-precision identification algorithms can be further employed to recognition the type of the problems. In this way, the problems occurred in the substations can be found and solved in time.

An Efficient Stream Data Processing Model for Multiuser Cryptographic Service

In view of the demand for high-concurrency massive data encryption and decryption application services in the security field, this paper proposes a dual-channel pipeline parallel data processing model (DPP) according to the characteristics of cryptographic operations and realized cryptographic operations of cross-data streams with different service requirements in a multiuser environment. By encapsulating cryptographic operation requirements in job packages, the input data flow is divided by the dual-channel mechanism and job packages parallel scheduling, which ensures the synchronization between the processing of the dependent job packages and parallel packages and hides the processing of the independent job package in the processing of the dependent job package. Prototyping experiments prove that this model can realize the correct and rapid processing of multiservice cross-data streams. Increasing the pipeline depth and improving the processing performance in each stage of the pipeline are the key to improving the system performance.

RESPONSIVE URBAN MODELS BY PROCESSING SETS OF HETEROGENEOUS DATA

Abstract. This paper presents some steps in experimentation aimed at describing urban spaces made following the series of earthquakes that affected a vast area of central Italy starting on 24 August 2016. More specifically, these spaces pertain to historical centres of limited size and case studies that can be called “problematic” (due to complex morphological and settlement conditions, because they are difficult to access, or because they have been affected by calamitous events, etc.). The main objectives were to verify the use of sets of heterogeneous data that are already largely available to define a workflow and develop procedures that would allow some of the steps to be automated as much as possible. The most general goal was to use the experimentation to define a methodology to approach the problem aimed at developing descriptive responsive models of the urban space, that is, morphological and computer-based models capable of being modified in relation to the constantly updated flow of input data.

Visualization of Process of Wheel Steel High Ingots Simulation

The mathematical model for computation of formation of wheel steel high ingots has been formulated based on the generalized system of equations consisting of the Navier-Stokes equation, the turbulent heat and mass transfer equation and the continuity equation. It is suggested to use a pattern when designing software for simulation of hydrodynamic and thermo-physical processes.A software complex with friendly input and output data flows is provided for technologists of metallurgical production.

The ATLAS Event Service: A new approach to event processing

The ATLAS Event Service (ES) implements a new fine grained approach to HEP event processing, designed to be agile and efficient in exploiting transient, short-lived resources such as HPC hole-filling, spot market commercial clouds, and volunteer computing. Input and output control and data flows, bookkeeping, monitoring, and data storage are all managed at the event level in an implementation capable of supporting ATLAS-scale distributed processing throughputs (about 4M CPU-hours/day). Input data flows utilize remote data repositories with no data locality or pre-staging requirements, minimizing the use of costly storage in favor of strongly leveraging powerful networks. Object stores provide a highly scalable means of remotely storing the quasi-continuous, fine grained outputs that give ES based applications a very light data footprint on a processing resource, and ensure negligible losses should the resource suddenly vanish. We will describe the motivations for the ES system, its unique features and capabilities, its architecture and the highly scalable tools and technologies employed in its implementation, and its applications in ATLAS processing on HPCs, commercial cloud resources, volunteer computing, and grid resources.Notice: This manuscript has been authored by employees of Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy. The publisher by accepting the manuscript for publication acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.