THE EFFECT OF DIFFERENT OCCUPATIONAL BACKGROUND NOISES ON VOICE RECOGNITION ACCURACY

Voice recognition has become an integral part of our lives, commonly used in call centers and as part of virtual assistants. However, voice recognition is increasingly applied to more industrial uses. Each of these use cases has unique characteristics that may impact the effectiveness of voice recognition, which could impact industrial productivity, performance, or even safety. One of the most prominent among them is the unique background noises that are dominant in each industry. The existence of different machinery and different work layouts are primary contributors to this. Another important characteristic is the type of communication that is present in these settings. Daily communication often involves longer sentences uttered under relatively silent conditions, whereas communication in industrial settings is often short and conducted in loud conditions. In this study, we demonstrated the importance of taking these two elements into account by comparing the performances of two voice recognition algorithms under several background noise conditions: a regular Convolutional Neural Network (CNN) based voice recognition algorithm to an Auto Speech Recognition (ASR) based model with a denoising module. Our results indicate that there is a significant performance drop between the typical background noise use (white noise) and the rest of the background noises. Also, our custom ASR model with the denoising module outperformed the CNN based model with an overall performance increase between 14-35% across all background noises. . Both results give proof that specialized voice recognition algorithms need to be developed for these environments to reliably deploy them as control mechanisms.

Industrial Relations and Labour Management and Productivity: The Imperative for Sustainable Development in Nigeria

Industrial relations, labour management and productivity have their roots in the industrial revolution which created the modern labour relationship by spawning large-scale industrial organizations. As society wrestled with these massive economic and social changes, labour problems aroused coupled with societal reconstruction challenges. Premised on this background, this paper is set to discuss the conceptual meaning of labour and industrial relations, assess the roles and prospects of labour in Nigeria, examine the consequential effects of labour-industrial relations and examine challenges of labour productivity and management in Nigeria. The paper also discusses some frameworks for labour-industrial relations. It focuses attention on the changing structure of the labour environment and the rise of precarious working conditions orchestrated by various unrests and acrimonies from nonchalant attitudes and behaviours of government and private sectors towards labour/workers’ welfare and patronage. The data for this study were collected through secondary sources. The secondary data were obtained from textbooks, journals, newspapers, internet materials and literature from academic journals in relation to the subject studied. The study adopted Industrial Relations Theory as a theoretical framework. The paper concludes that labour and industrial relations are part of the critical factors and are tools in advancing industrial productivity and attaining sustainable development in Nigeria.

Design and Implementation of a Low-Cost Microcontroller-Based an Industrial Delta Robot

This paper presents a practical design and control for a delta robot based on a low-cost microcontroller. The main purpose of the proposed delta robot is to improve and enhance industrial productivity such as fast pick-and-place tasks and fully autonomous production lines. Additionally, during a global pandemic similar to (COVID-19), some medical and food products suffer from a sudden increase and demand. Moreover, kinematics, workspace dynamics analysis took into consideration an optimized approach to achieve a viable yet efficient model representing them. Furthermore, stress analysis and material selection have been applied, targeting to achieve high customizability of the manipulator linages. Taking availability into considerations, most components are available locally for ease of manufacturing. To add a touch of machine vision to the robot, a camera module is mounted in an optimized fashion to optimize the robot's performance and increase its accuracy. Finally, various interchangeable end effectors can be mounted including a magnetic gripper, vacuum suction cup, soft-robotics grippers, and other types to suit our requirements and needs.

Design of Customized Intelligent Manufacturing Information Interaction System Based on Virtual Technology

With the continuous development of information technology, system intelligence is leading the next round of “industrial revolution”, especially the intelligent manufacturing industry has become the core of improving industrial productivity. Intelligent manufacturing involves each link in the manufacturing industry, which is the most critical part of intelligent manufacturing is intelligent production, through intelligent manufacturing related technology to optimize the production mode of manufacturing to promote the production state more flexible and integrated. Intelligent manufacturing is based on computer simulation technology and information and communication technology, optimize the production design of the factory and simplify the production process of the factory, the purpose is to reduce the waste of resources and improve the reasonable allocation of production resources.

COVID-19, POLICY RESPONSES, AND INDUSTRIAL PRODUCTIVITY AROUND THE GLOBE

We examine whether the COVID-19-induced policy responses by countries moderated the negative impact of the pandemic on industrial productivity. Using a panel of the 50 most affected countries by the pandemic, we show that the policy responses do not only help reduce the spread of COVID-19, but they also moderate its negative impact on industrial productivity and help steer countries back to their growth paths. We demonstrate that, in the absence of the pandemic, some of the policy responses (i.e., lockdowns, travel restrictions, etc.) would have reduced productivity. We further demonstrate that our estimates are robust when considering alternative specifications of our productivity model. Our study provides strong support for evidence-based policies and emphasizes, consistent with theoretical arguments, that an optimal policymix is fundamental to steering economies back to their steady productivity growth paths when facing negative shocks.

An Investigation into the Channel of Public Expenditure to Boost Industrial Productivity in Pakistan

It is a fact that public expenditure has a strong association with industrial productivity. The industrial sector recorded slow growth of 5.43%, which adds 20.90% to the GDP of Pakistan (2017-2018). This study aims to find the effects of public expenditure on Total Factor Productivity (TFP) in the industrial sector of the country. The study constructed two different models. In the first model, the study used time series data from 1975 to 2018, and the growth of adjusted TFP was calculated by the growth accounting method. In the second model, the study collected data from 1977 to 2018 and checked the impact of government expenditure on the TFP growth in the industry. The unit root tests, Ordinary Least Square (OLS), and Vector Error Correction Model (VECM) were employed. The findings of the study revealed that public expenditures on education were significant and positively related to TFP growth in industries. Public expenditure on health, agriculture, and inflation had a significant and positive association with TFP growth in the industries. Foreign direct investment had a negative but significant impact on TFP growth. The results of the present study suggest that industrial productivity can be increased by increasing the expenditure on education and health.

Computer Aided System for Industrial Productivity Performance Determination

One obvious problem in determining productivity especially for small and medium scale companies is the lack of appropriate software to help determine their productivity. This work developed a computer aided system for industrial productivity performance determination for small and medium-scale industries. The industrial productivity assessment application was developed using C# in the visual studio Integrated development environment. The application featured an easy to use and modern graphical interface and PDF report generation functionality. The usage procedure basically involved getting the productivity input data via an administered questionnaire, feeding the input into the Material, Capital, Labour, Energy, Expenses and Output sections respectively and generating the productivity report. In order to evaluate the performance of the developed application, a suitable company “Psaltry International Ltd” was selected as a case study and the data from 2013 to 2019 were used as the test inputs. The input data were also analysed manually and the result were compared with the generated results from the productivity assessment application which duly showed that the result from the productivity application was accurate. Based on this it was recommended that small and medium scale industries should embrace the use of computer aid systems in the management of their productivity.

Recent Automation Trends in Portugal: Implications on Industrial Productivity and Employment in Automotive Sector

Recent developments in automation and artificial intelligence (AI) are leading to a wave of innovation in organizational design and changes in the workplace. Techno-optimists even named it the “second machine age,” arguing that it now involves the substitution of the human brain. Other authors see this as just a continuation of previous ICT developments. Potentially, automation and AI can have significant technical, economic, and social implications in firms. This paper will answer the following question: What are the implications on industrial productivity and employment in the automotive sector with the recent automation trends, including AI, in Portugal? Our approach used mixed methods to conduct statistical analyses of relevant databases and interviews with experts on R&D projects related to automation and AI implementation. Results suggest that automation can have widespread adoption in the short term in the automotive sector, but AI technologies will take more time to be adopted. The findings show that adoption of automation and AI increases productivity in firms and is dephased in time with employment implications. Investments in automation are not substituting operators but rather changing work organization. Thus, negative effects of technology and unemployment were not substantiated by our results.

Recent automation trends in Portugal: implications on industrial productivity and employment in automotive sector

Artificial Intelligence (AI) is an automation mechanism that runs in a computer system performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision making or translation [1]. Some authors argue that recent developments in AI are leading to a wave of innovation in organizational design and changes to institutionalized norms of the workplace [2]. Techno-optimists even named this present phase the ‘second machine age’, arguing that it now involves the substitution of the human brain (Brynjolfsson and McAfee 2014). Potentially, the ability to apply AI in a generalized way can produce significant technical, economic and social effects in firms. But how many of these AI applications are ready and how far can they be from reaching the manufacturing industry market? The paper will answer the question: what are the implications on industrial productivity and employment in the automotive sector with the recent automation trends in Portugal? We will focus on AI as the most relevant emergent technology to understand the development of automation in areas related to robotics, software, and data communications in Europe (Moniz 2018). R&D investments in industrial processes in general may reflect productivity improvements derived from the increased automation process. Our results will be based on case studies from the automotive and components sector combined with database search by keywords that signal intelligence automation developments and AI applications selected from national R&D projects (on robotics, machine learning, collaborative tools, human-machine interaction, autonomous systems, etc) supported by European structural funds. The implications on industrial productivity and employment will be discussed in relation to automation trends in the automotive sector.