An Optimized Algorithm and Test Bed for Improvement of Efficiency of ESS and Energy Use

Prioritization of important factors towards the status of industry 4.0 implementation utilizing AHP and ANP techniques

Purpose

The purpose of the paper is to visualize the priorities of important factors towards the status of Industry 4.0 implementation in Indian manufacturing industries by utilizing the analytic hierarchy process (AHP) and analytic network process (ANP) techniques.

Design/methodology/approach

Based on a review of relevant literature, the authors recognized four criteria and 16 critical factors that have been validated by academic and industry experts. AHP and ANP models are then developed to evaluate the requirements and essential elements of the Industry 4.0 implementation. The outcomes were validated with the aid of sensitivity analysis.

Findings

The above models offer the industry a clear and straightforward way to understand the critical factors in the status of Industry 4.0 implementation. The two techniques have emerged to be influential in deciding the status of Industry 4.0 implementation. The two methods yielded almost identical outcomes. Both methods take into account the industry's specific needs based on their available capacity.

Practical implications

Decision-makers and specialists will use the strategies discussed in this paper to effectively include the implementation status of Industry 4.0 in their industries, based on their ability to make arrangements for proper implementation of Industry 4.0 and to concentrate on top priority factors when implementing Industry 4.0 at their workplace.

Originality/value

The contribution of this research is that it is the first to be carried out with a view to both AHP and ANP to analyze important factors regarding the implementation status of Industry 4.0 and authentication through sensitivity analysis methods; this is a recent initiative in Industry 4.0.

An Approach to Hyperparameter Optimization for the Objective Function in Machine Learning

In machine learning, performance is of great value. However, each learning process requires much time and effort in setting each parameter. The critical problem in machine learning is determining the hyperparameters, such as the learning rate, mini-batch size, and regularization coefficient. In particular, we focus on the learning rate, which is directly related to learning efficiency and performance. Bayesian optimization using a Gaussian Process is common for this purpose. In this paper, based on Bayesian optimization, we attempt to optimize the hyperparameters automatically by utilizing a Gamma distribution, instead of a Gaussian distribution, to improve the training performance of predicting image discrimination. As a result, our proposed method proves to be more reasonable and efficient in the estimation of learning rate when training the data, and can be useful in machine learning.

A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods

From the growth of residential energy demands has emerged new approaches for load scheduling to realize better energy consumption by shifting the required demand in response to cost changes or incentive offers. In this paper, a hybrid method is proposed to optimize the load scheduling problem for cost and energy saving. The method comprises a multi-objective optimization differential evolution (MODE) algorithm to obtain a set of optimal solutions by minimizing the cost and peak of a load simultaneously, as a multi-objective function. Next, an integration of the analytic hierarchy process (AHP) and a technique for order preferences by similarity to ideal solution (TOPSIS) methods are used as multi-criteria decision making (MCDM) methods for sorting the optimal solutions’ set from the best to the worst, to enable the customer to choose the appropriate schedule time. The solutions are sorted based on the load peak and energy cost as multi-criteria. Data are for ten appliances of a household used for 24 h with a one-minute time slot. The results of the proposed method demonstrate both energy and cost savings of around 47% and 46%, respectively. Furthermore, the results are compared with other recent methods in the literature to show the superiority of the proposed method.

Drivers and Barriers in Using Industry 4.0: A Perspective of SMEs in Romania

Considering the worldwide evolutionary stage of Industry 4.0, this study wants to fill in a lack of information and decision-making, trying to answer a question about the level of preparation of Romanian Small and Medium-sized Enterprises (SMEs) regarding the implementation of the new technology. The main purpose of this article is to identify the opinions and perceptions of SME managers in Romania on the drivers and barriers of implementing Industry 4.0 technology for business development. The research method used in the study was analyzed by sampling using the questionnaire as a data collection tool. It includes closed questions, measured with a nominal and orderly scale. 176 managers provided complete and useful answers to this research. The collected data were analyzed with the Statistical Package for the Social Sciences (SPSS) package using frequency tables, contingency tables, and main component analysis. Major contributions from research have highlighted the fact that Romania is in a full transition process from industry 2.0 to industry 4.0. There was also a high level of knowledge of the new Industry 4.0 technology, and a desire to implement it in the Romanian SMEs, as well as the low level of resources needed to implement it.