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A method for the synthesis of dynamic control is presented. The method is based on an exact modelling of manipulator dynamics and a relatively simple synthesis of control algorithms. The authors have applied the method to a UMS‐2 industrial robot.

In the paper is presented the practical application of the manipulator automatic two‐level control concept. Anthropomorphic configuration of the system was chosen, consisting of the minimal configuration, which solves the task of attaining the position and the gripper, which has to satisfy correct orientation in approaching the working object, as well as manipulation of the same. The manipulator configuration, chosen in that way, was applied to the concrete task of final treatment of the thermostatic element for the automobile industry. Realization project of this system contains three basic elements:

The purpose of this paper is to design a novel optimized biped robot gait generator which plays an important role in helping the robot to move forward stably. Based on a mathematical point of view, the gait design problem is investigated as a constrained optimum problem. Then the task to be solved is closely related to the evolutionary calculation technique.

Based on this fact, this paper proposes a new way to optimize the biped gait design for humanoid robots that allows stable stepping with preset foot-lifting magnitude. The newly proposed central force optimization (CFO) algorithm is used to optimize the biped gait parameters to help a nonlinear uncertain humanoid robot walk robustly and steadily. The efficiency of the proposed method is compared with the genetic algorithm, particle swarm optimization and improved differential evolution algorithm (modified differential evolution).

The simulated and experimental results carried out on the small-sized nonlinear uncertain humanoid robot clearly demonstrate that the novel algorithm offers an efficient and stable gait for humanoid robots with respect to accurate preset foot-lifting magnitude.

This paper proposes a new algorithm based on four key gait parameters that enable dynamic equilibrium in stable walking for nonlinear uncertain humanoid robots of which gait parameters are initiatively optimized with CFO algorithm.

This paper contributes to the problem of humanoid robot gait generation in unknown environments. The intention of the proposed method is to create an autonomous humanoid robot, able to take decisions and generate the appropriate optimal gait based on the information received by the eye system. Up to now, we have created two modules: walking and going upstairs. In order to create an autonomous humanoid robot, we plan to consider other tasks like going downstairs, creeping, obstacle overcoming, etc. In this paper, we present the simulation and experimental results for real time humanoid robot gait generation realized with the “Bonten‐Maru I” humanoid robot. The results showed that the Neural Network modules generate in a very short time a stable humanoid robot motion.

Going upstairs is a common humanoid robot task. In this paper, a genetic algorithm (GA) gait synthesis method for going upstairs and a radial basis function neural network (RBFNN) implementation, are considered. The gait synthesis is analyzed based on the minimum consumed energy and minimum torque change. The proposed method can easily be applied to generate the angle trajectories for going downstairs, overcoming obstacles, etc. In our work, the stability is verified through the ZMP concept. For the real time implementation, a RBFNN which is taught based on the GA results, is considered. The RBFNN generates the optimal gait in a very short time, where the input variables are the step length, step height and step time. Simulations are realized based on the parameters of the “Bonten‐Maru I” humanoid robot.

The chapter discusses the conditions under which a transport small and medium-sized enterprise (SME) engages in strategic corporate social responsibility (CSR) and how complex new technology might be adopted.

The chapter is based on the case study of a transport SME engaging into greening its transport activity with electric trucks.

The case study gives an insight into adoption conditions of radical new technology by an SME where collective adoption is required to make the new system efficient. It suggests that an industry leader undergoes the trial stage to favour later infusion of the innovation across the given industry. It is also shown that strategic CSR from SMEs is hampered by the complex setting into which new technologies are embedded.

Environmental concerns engage companies in important technological changes. In order to set up their strategy, SMEs should get involved in professional organizations within their industry and across industries, to benefit from information and ultimately to facilitate adoption where multi-user adoption is a key technology characteristic.

The work introduces the challenges of transition economy and new technology adoption in the prospect of greening transport activities. It shows the eco-systemic nature of ongoing changes and the necessity for SMEs to network within and across industries.

This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems.

Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems.

The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support.

Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems.

Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support.

This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making.

This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.

Tourism has become one of the world's major industries measured in terms of turnover, the number of employees and foreign currency earnings, having at the same time a huge impact on the environment. However, the future development of tourism depends on today's decisions that often do not take into account the positive and the negative impact on the tourism destinations' environment with long-term consequences that are not easily undone. System archetypes are generic structures that are responsible for generic patterns of behavior over time, especially behavior that is counterintuitive. The article aims to explore the possible use of system archetypes in order to increase socially responsible (SR), i.e. systemic thinking and behavior of tourism business organizations, based on the requisite holism theory.

The experimental design methodology has been used in order to test the assumption that individuals familiar with the system archetypes will be more likely to believe that tourism business organizations that operate in tourism destinations should be involved in attaining SR goals. Participants included managers, public authorities and community representatives and were divided into an experiment and a control group. The experiment group was exposed to a workshop on system archetypes, while the control group had no treatment. Structured quantitative interviews were used in order to test differences in attitudes and beliefs on SR of tourism business organizations among the experiment and the control group members.

Natural environment of tourism destinations as tourism's essential resource is often destroyed due to the lack of SR. The research results indicate that the exposure of individuals to system archetypes increased the understanding of importance of SR behavior of tourism business organizations. System archetypes increased individuals' comprehension of the fact that the non-linear causal relationship, time delay and hidden structures of the systems generate complex behavior resulting in damage to the natural environment of tourism destinations.

The survey research on a restricted number of subjects was applied. In order to overcome limitations of such an approach, the rigor procedure for data collection and analysis was used.

Managers of tourism business organizations could use system archetypes to analyse the impact of their activities on tourism destinations environment and thus improve the social responsibility of their decisions. The authors propose the formation of system archetypes and their applications repository in an organized environment to enhance understanding of SR behavior of tourism business organizations.

The authors used experimental design in order to test whether exposure of stakeholders' to system archetypes changes their attitudes regarding SR, with the goal to increase the understanding of various conflicts that emerge from the fact that tourism depends on unspoiled environment and at the same time tourism is a potential polluter. Available literature offers no similar applications of the system archetype approach to systemic behavior via SR in tourism.

This paper aims to investigate whether multinational enterprises (MNEs) are more or less likely than local firms to violate their employees’ human rights in emerging economies, whether regional institutional pressures influence the likelihood of violating employee human rights and whether the density of MNEs in a region affects the likelihood of employees’ human rights violation by local firms.

Building on neo-institutional theory, this paper hypothesizes that, in an emerging economy, MNEs violate their employees’ human rights significantly less than local firms do. Moreover, it is hypothesized that the quality of regional institutions only influences the social behavior of local firms toward their employees. In addition, it is hypothesized that the density of MNEs in a region has a positive effect on local firms’ attitudes toward employee human rights. These hypotheses are examined using a sample of 1,211,638 respondent–year observations in 32 Mexican regions between 2005 and 2014.

This paper shows that MNEs are less likely to violate their employees’ human rights than local firms are. It also provides evidence that regional institutions do not influence MNE behavior toward employee human rights violation, but affect local firms. Furthermore, contrary to what was hypothesized, the density of MNEs in a region has a negative rather than positive influence on local firms’ respect of employee human rights.

This paper advances understanding of the behavior of MNEs in an emerging economy setting and contributes to the ongoing debate in the literature on their social impact.

This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation.

A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/optimisation of energy efficiency.

An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add-on to currently used energy management systems.

The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described).

A decision support system has been created as an innovative add-on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved.

For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge-based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.