Agora University Editing House: Journals
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Supplier Selection Model Based on D Numbers and Transformation Function
Full text linkSelecting reasonable suppliers can effectively improve the efficiency of enterprise supply chain management. Among them, expert evaluation is an important part of supplier selection problem, but the uncertainty, fuzziness and incompleteness of expert opinions make supplier selection problem difficult to solve. In order to systematically and effectively solve the uncertainty, ambiguity and incompleteness in supplier selection problem, this paper presents a new supplier selection method based on D numbers and transformation function. First, fuzzy preference relation is generated based on the decision matrix of pairwise comparisons given by experts. D numbers which can effectively deal with uncertain information extend fuzzy preference relation (D matrix). Second, the D matrix is converted into a crisp matrix form based on the integration representation of D numbers according to different situations whether or not the information in D matrix is complete. Third, the crisp matrix is converted into judgement matrix by using the transformation functions. Finally, analytic hierarchy process (AHP) method is applied based on the judgment matrix to give a priority weights for decision making. Three numerical examples and application of the supplier selection are used to show the feasibility and effectiveness of the proposed method
Substantial Phase Exploration for Intuiting Covid using form Expedient with Variance Sensor
Full text linkThis article focuses on implementing wireless sensors for monitoring exact distance between two individuals and to check whether everybody have sanitized their hands for stopping the spread of Corona Virus Disease (COVID). The idea behind this method is executed by implementing an objective function which focuses on maximizing distance, energy of nodes and minimizing the cost of implementation. Also, the proposed model is integrated with a variance detector which is denoted as Controlled Incongruity Algorithm (CIA). This variance detector is will sense the value and it will report to an online monitoring system named Things speak and for visualizing the sensed values it will be simulated using MATLAB. Even loss which is produced by sensors is found to be low when CIA is implemented. To validate the efficiency of proposed method it has been compared with prevailing methods and results prove that the better performance is obtained and the proposed method is improved by 76.8% than other outcomes observed from existing literatures
Fault Detection in Three-phase Induction Motor based on Data Acquisition and ANN based Data Processing
Full text linkThe main objective of this paper is to investigate how a failure in the functioning of a normal electrical system represented by a three-phase asynchronous motor will modify the voltages and currents present in the system and if it is possible to design a system that is able to automatically detect the fault, based on the use of modern data acquisition system and powerful computer processing capabilities. The detection of faulty signals is realised using Feedforward Artificial Neural Networks
Decision Support Model for Raw Water Availability for Purification in a Region in Chile
Full text linkThis article proposes a decision model to identify the most sustainable solution(s) to ensure the availability of raw water to be subsequently treated to be converted into drinking water as a consequence of the climate change scenario, particularly the drought currently experienced by the Metropolitan Region in Chile, derived from the technical and regulatory requirements associated with the availability of water resources from its capture to its drink ability to meet the future demand of the region. From the perspective of drought, the solution must provide security levels that guarantee the availability of raw water is one of the main concerns of the stakeholders. In turn, the need to adapt current regulations regarding raw water sources, as well as community acceptance of some proposals for converting raw water into potable water and climate dependency, involve qualitative as well as technical aspects that may affect the investment and operating costs of the different solutions required to ensure raw water availability. Therefore, through a multi-criteria approach, it is possible to incorporate quantifiable and intangible aspects and to address conflicting objectives. Through a case study, we present a decision model based on the Analytic Hierarchy Process to define and evaluate the most sustainable solution(s) to secure raw water for drinking. This study proposes to integrate technical and qualitative attributes to identify the challenging criteria and the associated linkage to the problem of selecting proposals for the most sustainable solution(s) to secure raw water, being a guide to decide the implementation of the most appropriate solution
Marketing influencer
Full text linkIs marketing influence the right way or not for a business? Investing in this type of marketing is advantageous or not for promoting a product or service. What type of marketing defines this strategy? We will discuss all these aspects in this paper
IoT-inspired Framework for Real-time Prediction of Forest Fire
Full text linkWildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniqueS
Analysis of a Public and Private Networks for Nutrient Measurement System using LoRawan Network
Full text linkLorawan network is ideal for IoT devices that continuously monitor a device and provide information to the gateway if the monitored data is outside the permitted threshold. These devices only require a small bandwidth and are therefore capable of operating on batteries for a long period of time. This study evaluates the design of a tool to measure soil nutrients with parameters of Nitrogen (N), Phosphorus (P), Potassium (K) using NPK sensors and IoT-based systems. The microcontroller used is ESP 32 which is connected to two types of networks. And will be integrated by Antares and the Android app. The purpose of making two types of networks in order to obtain data for analysis or development of the next tool. The result of designing this system is to create a device that can help farmers or the community in the process of measuring nitrogen, phosphorus, and potassium levels directly through the Android application so that soil control and fertilization can be more effective moreover yields can be maximized
Fault Detection in Nuclear Power Plants using Deep Leaning based Image Classification with Imaged Time-series Data
Full text linkFault detection is critical to ensure the safely routine operations in nuclear power plants (NPPs), requiring very high accuracy and efficiency. Meanwhile, the rapid development of modern information technologies have profoundly changed and promoted various sectors including nuclear industry. Inspired by the great progress and promising performance of deep learning based image classification recent years, a two-stage fault detection methodology in NPPs has been proposed in this paper. First the time-series data describing the operating status of NPPs have been transformed into two-dimensional images by four methods, preserving the time-series information in images and converting the fault detection problem into a supervised image classification task. Then four specific image classifying models based on three primary deep learning architectures have been separately experimented on the imaged time-series data, achieving excellent accuracies. Further the performances of different combinations of transforming means and classifying models have been compared and discussed with extensive experiments and detailed analysis of throughput for four transforming methods. This methodology proposed has obtained remarkable results by reshaping data format and structure, making image classifying models applicable, which not only efficiently detect and warn possible faults in NPPs but also enhances the capability for safety management in nuclear power systems
A Stochastic Mobility Prediction Algorithm for finding Delay and Energy Efficient Routing Paths considering Movement Patterns in Mobile IoT Networks
Full text linkIn Mobile IoT Networks, the network nodes are constantly moving in a field, causing interruptions in the communication paths and, thus, generating long delays at the time of building a communication path from a source IoT node to the gateway (destination node). Communication interruptions affect the delay performance in delay-sensitive applications such as health and military scenarios. In addition, these IoT nodes are equipped with batteries, whereby it is also necessary to accomplish energy consumption requirements. In summary, a gateway node should not receive messages or packets coming from the IoT nodes with undesired delays, whereby it is pertinent to propose new algorithms or techniques for minimizing the delay and energy consumption experimented in the IoT network. Due to IoT nodes are attached to humans, animals or objects, they present a specific movement pattern that can be analyzed to improve the path-building with the aim of reducing the end-to-end delay. Therefore, we propose the usage of a mobility prediction technique based on a Stochastic Model to predict nodes’ positions in order to obtain minimum cost paths in terms of energy consumption and delay in mobile IoT networks. Our stochastic model is tuned and evaluated under the Markov-Gauss mobility model, considering different levels of movement randomness in order to test how the capability prediction of our proposal can impact the delay and energy consumption in mobile IoT networks in comparison with others routing algorithms
AN EMPIRICAL STUDY OF EMBEZZLEMENT CASES IN CHINA
Full text linkUsing the method of empirical research, this paper selects 194 valid judgments of embezzlement cases published by Chinese officials in 2019 as samples to explore the current situation of the subject, object and sentencing results of embezzlement cases. The analysis discloses some characteristics such as high educational level of criminals and high proportion of state organ staffs and grass-roots public servants. At the same time, many problems are revealed, such as the division of powers and responsibilities within the enterprises is not clear, as well as the external supervision in key fields is not enough, which all provide conditions for embezzlement crime. In addition, the phenomenon of unfair sentencing is obvious, and the sentencing mode is not accurately applied in judicial practice at this stage. Therefore, a more reasonable, accurate and effective embezzlement crime prevention system needs to be established in the future