Parallel Network System Research Articles

Evaluating the performance of intercity road freight transport: Double-frontier parallel network cross-efficiency model

Performance assessment of Intercity Road Freight Transport (IRFT) involve a complex set of considerations to ensure the efficient transportation of goods between their origins and destinations. Notably, Data Envelopment Analysis (DEA) models are often the preferred approach for conducting assessment studies that involve multiple inputs and outputs. Even though the conventional Cross-Efficiency (CE) technique addresses the limitations of DEA in producing comparable results, it still fails to consider the evaluation of internal system operations. The use of Network CE leads to more realistic results in assessing problems. It addresses not only the limitations of the DEA model in producing comparable results but also considers the performance of internal operations and system efficiency. Incorporating CE into a Parallel Network System (PNS), which is known as CE-PNS, has also a significant drawback as it neglects both the optimistic and pessimistic viewpoints based on the efficient and anti-efficient frontiers simultaneously, and may therefore fail to provide a comprehensive analysis. The primary objective of this study is to propose a new double-frontier network CE that takes into account two perspectives simultaneously. Indeed, this study is the first to incorporate the concepts of CE and double-frontier DEA into a PNS to provide an effective tool for assessing Iranian IRFT. For this purpose, the Cross-Inefficiency (CIE) technique is first incorporated into the PNS, called CIE-PNS, which explores the relationship between system inefficiency and internal divisions. The CE-PNS and CIE-PNS may yield different results. Therefore, Double-Frontier CE for a PNS (DFCE-PNS) is derived by combining the efficiency results obtained from CE-PNS and CIE-PNS. This study also examines different inputs and outputs to assess the performance of Iranian IRFT. The results demonstrate that DFCE-PNS yields more comprehensive and reliable results.

The Structure Fault Tolerance of Alternating Group Networks

The alternating group network [Formula: see text] can be used to model the topology structure of a large-scale parallel network system. In this work, the structure fault tolerance of alternating group networks based on the star and path structures is investigated. For a graph [Formula: see text] and its a connected subgraph [Formula: see text], the [Formula: see text]-structure connectivity [Formula: see text] (resp. [Formula: see text]-substructure connectivity [Formula: see text]) of [Formula: see text] is the cardinality of a minimum family [Formula: see text] whose every element is isomorphic to [Formula: see text] (resp. isomorphic to a subgraph of [Formula: see text]) such that [Formula: see text] is disconnected. Specifically, we determine [Formula: see text] and [Formula: see text] for [Formula: see text].

Study on environmental performance evaluation of different linkage development types of the logistics and manufacturing industries considering the unexpected output

ABSTRACT According to the different organizational forms of logistics in manufacturing, the types of linkage development between the logistics and manufacturing industries include two different logistics service modes: logistics outsourcing and logistics self-operation. This study considers two manufacturing clusters for comparison: the northeast region in China, mainly state-owned and self-operated, and the Yangtze River Delta region in China, mainly focusing on logistics outsourcing services. First, we construct a parallel network system considering heterogeneity and an unexpected super-efficiency slacks-based measure: the Grey Relational Analysis model. Second, considering the unexpected output, this study evaluates the environmental performance of the linkage development of the logistics and manufacturing industries and explains the types of linkage development between them. Finally, the spatial and temporal characteristics of linkage efficiency under different linkage modes are studied using an exploratory spatial data analysis method. The research results show that the efficiency of the two-industry linkage in different linkage types that consider the unexpected output presents different development characteristics. Regarding time characteristics and trends, we compare the development efficiency of the two-industry linkage at the overall level of the northeast and the Yangtze River Delta regions from 2009 to 2016. The Yangtze River Delta region showed a higher state of linkage while the northeast region showed a medium linkage state. Regarding spatial pattern evolution, the effect of the technical efficiency of the linkage between the two industries in the three northeastern provinces showed continuous evolution from the medium linkage stage to the weak linkage stage, and the Yangtze River Delta showed continuous evolution from the medium linkage to the strong linkage stage. Implications: Due to data availability, references to the relevant achievements of the research on the linkage between the manufacturing and logistics industries, the transportation industry, warehousing, and postal and telecommunications industry data are generally used as logistics industry data. Manufacturing data are replaced by industrial data. The manufacturing industry is the core industry of industrial systems. Its output value accounts for more than 90% of the industrial output. Thus, it can reflect the development trend of the manufacturing industry in general.

A CURE Algorithm for Vietnamese Sentiment Classification in a Parallel Environment

Solutions to process big data are imperative and beneficial for numerous fields of research and commercial applications. Thus, a new model has been proposed in this paper to be used for big data set sentiment classification in the Cloudera parallel network environment. Clustering Using Representatives (CURE), combined with Hadoop MAP (M) / REDUCE (R) in Cloudera – a parallel network system, was used for 20,000 documents in a Vietnamese testing data set. The testing data set included 10,000 positive Vietnamese documents and 10,000 negative ones. After testing our new model on the data set, a 62.92% accuracy rate of sentiment classification was achieved. Although our data set is small, this proposed model is able to process millions of Vietnamese documents, in addition to data in other languages, to shorten the execution time in the distributed environment

A Reformed K-Nearest Neighbors Algorithm for Big Data Sets

A Data Mining Has Already Had Many Algorithms Which A K-Nearest Neighbors Algorithm, K-NN, Is A Famous Algorithm For Researchers. K-NN Is Very Effective On Small Data Sets, However It Takes A Lot Of Time To Run On Big Datasets. Today, Data Sets Often Have Millions Of Data Records, Hence, It Is Difficult To Implement K-NN On Big Data. In This Research, We Propose An Improvement To K-NN To Process Big Datasets In A Shortened Execution Time. The Reformed K-Nearest Neighbors Algorithm (R-K-NN) Can Be Implemented On Large Datasets With Millions Or Even Billions Of Data Records. R-K-NN Is Tested On A Data Set With 500,000 Records. The Execution Time Of R-K-NN Is Much Shorter Than That Of K-NN. In Addition, R-K-NN Is Implemented In A Parallel Network System With Hadoop Map (M) And Hadoop Reduce (R).

DEA for non-homogenous parallel networks

In practice, systems are often composed of a group of sub-units. Each sub-unit has a set of performance metrics that are classified as inputs and outputs in data envelopment analysis (DEA). Conventional DEA views such a system as a “black-box”, other DEA-based models are developed to investigate the inner structure, either with a serial structure where components are connected by intermediate products, or with a parallel system under the key assumption that all sub-units are associated with the same type of inputs and outputs (in differing amounts) without the links. In many applications, however, this property of identical input/output factors may not hold. For example, factories may have various manufacturing lines whose inputs and outputs differ from one another. The current paper proposes a series of DEA models to accommodate settings where non-homogenous sub-units operate in parallel network structures with intermediate measures or links. Both the overall performance of the entire parallel network system and efficiency decomposition for each sub-unit can be evaluated through our method.

The allocation of sub-decision making units to parallel fuzzy network systems

Purpose – The purpose of this paper is to consider the following problem; if the manager of the parallel network systems wants to add new sub-decision making units (sub-DMUs) to each parallel network system, he/she wants to know how much new fuzzy inputs allocate to new sub-DMUs and how much outputs these new sub-DMUs produce such that the efficiency of each parallel network system improve or preserve. Design/methodology/approach – Resource allocation and target setting is a famous topic in management science, therefore many managers attention to this field. Data envelopment analysis is one approach to apply the resource. Resource allocation and target setting is a famous topic in management science, therefore many managers attention to this field. Data envelopment analysis is one approach to apply the resource allocation and target setting. In real application the structure of many DMUs are network and the data of them are imprecise. In this work first the authors calculate the fuzzy efficiency of parallel systems by common set of weights method, after that the authors propose an approach to find how much fuzzy inputs allocate to new sub-DMUs, and how much new fuzzy outputs produce, where the efficiency of each parallel network system improve or preserve. Findings – It is found the value of inputs and outputs of new sub-DMUs, where the efficiency of each system not worse than before. Practical implications – The method can be used in many organizations such as banks, chain stores, car factory, etc. Originality/value – For the first time the authors allocate new sub-DMUs to each system, and the data of paper are fuzzy numbers.

A STUDY OF SELF-AWARENESS IN ROBOTS

The present paper studies self-awareness and introduces some self-awareness related incidents. It then describes the relationship between self-awareness and consciousness and explains the MoNAD, a neural network circuit developed by the authors that capably describes the phenomena of self-awareness and consciousness. A model of self-awareness is then presented. This self-awareness model is a parallel network system in which multiple independent MoNADs communicate with one another. In experiments with robots, three test robots were used: (1) a self-image robot reflected in a mirror, (2) another robot, and (3) a cable-connected robot behaving as commanded by the self-robot. The reactions of the three test robots to the self-robot were compared to investigate the self-awareness of the self-robot. The experiments have shown that the conditions required for the self-robot to interpret the test robot to be part of itself are: (1) the test robot must return a reaction within a certain period of time that is internally determined in the self-robot and (2) the returned reaction must be consistent with the expectation that is continually being calculated in the self-robot.

A parallel neural network approach to prediction of Parkinson’s Disease

Recently the neural network based diagnosis of medical diseases has taken a great deal of attention. In this paper a parallel feed-forward neural network structure is used in the prediction of Parkinson’s Disease. The main idea of this paper is using more than a unique neural network to reduce the possibility of decision with error. The output of each neural network is evaluated by using a rule-based system for the final decision. Another important point in this paper is that during the training process, unlearned data of each neural network is collected and used in the training set of the next neural network. The designed parallel network system significantly increased the robustness of the prediction. A set of nine parallel neural networks yielded an improvement of 8.4% on the prediction of Parkinson’s Disease compared to a single unique network. Furthermore, it is demonstrated that the designed system, to some extent, deals with the problems of imbalanced data sets.

Pattern recognition of guided waves for damage evaluation in bars

Guided waves damage identification in bars with neural networks acquires training data from simulation as a cost-effective measure. These neural networks applied with a novel test inputs dependent iterative training scheme are capable of quantifying damages accurately from experimental inputs. The reliability of the predictions depends on the quality of the measured signals, which can be increased by considering more than one signal obtained from different sensor locations or by changing the properties of the interrogation pulse. A parallel network system to process the inputs from these signals collaboratively is described. The core of the system is a data fusion process that associates overlapping intermediate test results while isolating outliers to narrow the training range for improved generalization in the iterative test inputs dependent training scheme. This robust system of signal processing has achieved accurate average damage quantitative results with errors below 4% and 13% the original size of the training parameter space for damage location and depth, respectively, of artificial laminar defects in bars.