Search results

The study aims to paint a representative picture of the current state of search interfaces of Swedish online museum collections, focussing on search functionalities with particular reference to subject searching, as well as the use of controlled vocabularies, with the purpose of identifying which improvements of the search interfaces are needed to ensure high-quality information retrieval for the end user.

In the first step, a set of 21 search interface criteria was identified, based on related research and current standards in the domain of cultural heritage knowledge organization. Secondly, a complete set of Swedish museums that provide online access to their collections was identified, comprising nine cross-search services and 91 individual museums' websites. These 100 websites were each evaluated against the 21 criteria, between 1 July and 31 August 2020.

Although many standards and guidelines are in place to ensure quality-controlled subject indexing, which in turn support information retrieval of relevant resources (as individual or full search results), the study shows that they are not broadly implemented, resulting in information retrieval failures for the end user. The study also demonstrates a strong need for the implementation of controlled vocabularies in these museums.

This study is a rare piece of research which examines subject searching in online museums; the 21 search criteria and their use in the analysis of the complete set of online collections of a country represents a considerable and unique contribution to the fields of knowledge organization and information retrieval of cultural heritage. Its particular value lies in showing how the needs of end users, many of which are documented and reflected in international standards and guidelines, should be taken into account in designing search tools for these museums; especially so in subject searching, which is the most complex and yet the most common type of search. Much effort has been invested into digitizing cultural heritage collections, but access to them is hindered by poor search functionality. This study identifies which are the most important aspects to improve.

State ownership has been common especially in industries with restricted competition. In Russia, state-controlled firms represent around 41 percent of the market value of all listed firms (Deloitte, 2015). Yet, there is a significant gap in the literature regarding the effects of various forms of government control in listed firms. The purpose of this paper is to fill this gap by exploring the impact of the complexity of state ownership and competition on the performance of Russian listed firms.

The sample consists of data for 72 firms (360 firm-years) in the Russian MOEX broad market index during 2011–2015. The complexity of state ownership is captured by studying forms of state control including majority/minority, direct/indirect, federal/regional, mixed structures and golden shares.

The authors find significant differences in performance relating to different forms of state ownership. State control is negatively related to firm valuation and the sales/employees ratio. Performance is weakest when state ownership takes the form minority, regional or direct ownership. State control through golden shares typically outperforms other state-controlled firms. The authors find indications of employment prioritization beyond the economical optimum. In addition, the relation between state ownership and profitability becomes positive in sectors where state firms appear to enjoy lower competition.

While the effects of state ownership have been studied on many markets, there is a lack of studies on the effects of different forms, or the complexity, of state ownership beyond direct and indirect ownership. The authors contribute to the literature on the performance effects of state ownership by studying a multitude of forms of governmental ownership as well as the role of competition in Russia. Especially the profitability of state-controlled firms is significantly affected by industry characteristics. Implications of the results are discussed both from firm and policy maker perspectives.

Two-handed automobile steering at low vehicle speeds may lead to reduced steering ability at large steering wheel angles and shoulder injury at high steering wheel rates (SWRs). As a first step toward solving these problems, this study aims, firstly, to design a surface electromyography (sEMG) controlled steering assistance interface that enables hands-free steering wheel rotation and, secondly, to validate the effect of this rotation on path-following accuracy.

A total of 24 drivers used biceps brachii sEMG signals to control the steering assistance interface at a maximized SWR in three driving simulator scenarios: U-turn, 90º turn and 45º turn. For comparison, the scenarios were repeated with a slower SWR and a game steering wheel in place of the steering assistance interface. The path-following accuracy of the steering assistance interface would be validated if it was at least comparable to that of the game steering wheel.

Overall, the steering assistance interface with a maximized SWR was comparable to a game steering wheel. For the U-turn, 90º turn and 45º turn, the sEMG-based human–machine interface (HMI) had median lateral errors of 0.55, 0.3 and 0.2 m, respectively, whereas the game steering wheel, respectively, had median lateral errors of 0.7, 0.4 and 0.3 m. The higher accuracy of the sEMG-based HMI was statistically significant in the case of the U-turn.

Although production automobiles do not use sEMG-based HMIs, and few studies have proposed sEMG controlled steering, the results of the current study warrant further development of a sEMG-based HMI for an actual automobile.

Small highly saturated interior permanent magnet- synchronous machines (IPMSMs) show a very nonlinear behaviour. Such machines are mostly controlled with a closed-loop cascade control, which is based on a d-q two-axis dynamic model with constant concentrated parameters to calculate the control parameters. This paper aims to present the identification of a complete current- and rotor position-dependent d-q dynamic model, which is derived by using a finite element method (FEM) simulation. The machine’s constant parameters are determined for an operation on the maximum torque per ampere (MTPA) curve. The obtained MTPA control performance was evaluated on the complete FEM-based nonlinear d-q model.

A FEM model was used to determine the nonlinear properties of the complete d-q dynamic model of the IPMSM. Furthermore, a fitting procedure based on the nonlinear MTPA curve is proposed to determine adequate constant parameters for MTPA operation of the IPMSM.

The current-dependent d-q dynamic model of the machine models the relevant dynamic behaviour of the complete current- and rotor position-dependent FEM-based d-q dynamic model. The most adequate control response was achieved while using the constant parameters fitted to the nonlinear MTPA curve by using the proposed method.

The effect on the motor’s steady-state and dynamic behaviour of differently complex d-q dynamic models was evaluated. A workflow to obtain constant set of parameters for the decoupled operation in the MTPA region was developed and their effect on the control response was analysed.

A standalone microgrid (MG) is able to use local renewable resources and reduce the loss in long distance transmission. But the single-phase device in a standalone MG can cause the voltage unbalance condition and additional power loss that reduces the cycle life of battery. This paper proposes an energy management strategy for the battery/supercapacitor (SC) hybrid energy storage system (HESS) to improve the transient performance of bus voltage under unbalanced load condition in a standalone AC microgrid (MG).

The SC has high power density and much more cycling times than battery and thus to be controlled to absorb the transient and unbalanced active power as well as the reactive power under unbalanced condition. Under the proposed energy management design, the battery only needs to generate balanced power to balance the steady state power demand. The energy management strategy for battery/SC HESS in a standalone AC MG is validated in simulation study using PSCAD/EMTDC.

The results show that the energy management strategy of HESS maintains the bus voltage and eliminates the unbalance condition under single-phase load. In addition, with the SC to absorb the reactive power and unbalanced active power, the unnecessary power loss in battery is reduced with shown less accumulate depth of discharge and higher average efficiency.

With this technology, the service life of the HESS can be extended and the total cost can be reduced.

This study aims to propose an offline exploratory method that consists of two stages: first, the authors focus on completing the kinematics model of the system by analyzing the Jacobians in the vicinity of the starting point and deducing a virtual input to effectively navigate the system along the non-holonomic constraint. Second, the authors explore the sensorimotor space in a predetermined pattern and obtain an approximate mapping from sensor space to chained form that facilitates controllability.

In this paper, the authors tackle the controller acquisition problem of unknown sensorimotor model in non-holonomic driftless systems. This feature is interesting to simplify and speed up the process of setting up industrial mobile robots with feedback controllers.

The authors validate the approach for the test case of the unicycle by controlling the system with time-state control policy. The authors present simulated and experimental results that show the effectiveness of the proposed method, and a comparison with the proximal policy optimization algorithm.

This research indicates clearly that feedback control of non-holonomic systems with uncertain kinematics and unknown sensor configuration is possible.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.