Search results

FastSLAM is a popular method to solve the problem of simultaneous localization and mapping (SLAM). However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in each particle. As a result, the execution speed will be too slow to achieve the objective of real-time navigation. Thus, this paper aims to improve the computational efficiency and estimation accuracy of conventional SLAM algorithms.

As an attempt to solve this problem, this paper presents a computationally efficient SLAM (CESLAM) algorithm, where odometer information is considered for updating the robot’s pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates.

Simulation results show that the proposed CESLAM can overcome the problem of heavy computational burden while improving the accuracy of localization and mapping building. To practically evaluate the performance of the proposed method, a Pioneer 3-DX robot with a Kinect sensor is used to develop an RGB-D-based computationally efficient visual SLAM (CEVSLAM) based on Speeded-Up Robust Features (SURF). Experimental results confirm that the proposed CEVSLAM system is capable of successfully estimating the robot pose and building the map with satisfactory accuracy.

The proposed CESLAM algorithm overcomes the problem of the time-consuming process because of unnecessary comparisons in existing FastSLAM algorithms. Simulations show that accuracy of robot pose and landmark estimation is greatly improved by the CESLAM. Combining CESLAM and SURF, the authors establish a CEVSLAM to significantly improve the estimation accuracy and computational efficiency. Practical experiments by using a Kinect visual sensor show that the variance and average error by using the proposed CEVSLAM are smaller than those by using the other visual SLAM algorithms.

This paper aims to demonstrate a cloud-based version of the improved Monte Carlo localization algorithm with robust orientation estimation (IMCLROE). The purpose of this system is to increase the accuracy and efficiency of indoor robot localization.

The cloud-based IMCLROE is constructed with a cloud–client architecture that distributes computation between servers and a client robot. The system operates in two phases: in the offline phase, two maps are built under the MapReduce framework. This framework allows parallel and even distribution of map information to a cloud database in pre-described formats. In the online phase, an Apache HBase is adopted to calculate a pose in-memory and promptly send the result to the client robot. To demonstrate the efficiency of the cloud-based IMCLROE, a two-step experiment is conducted: first, a mobile robot implemented with a non-cloud IMCLROE and a UDOO single-board computer is tested for its efficiency on pose-estimation accuracy. Then, a cloud-based IMCLROE is implemented on a cloud–client architecture to demonstrate its efficiency on both pose-estimation accuracy and computation ability.

For indoor localization, the cloud-based IMCLROE is much more effective in acquiring pose-estimation accuracy and relieving computation burden than the non-cloud system.

The cloud-based IMCLROE achieves efficiency of indoor localization by using three innovative strategies: firstly, with the help of orientation estimation and weight calculation (OEWC), the system can sort out the best orientation. Secondly, the system reduces computation burden with map pre-caching. Thirdly, the cloud–client architecture distributes computation between the servers and client robot. Finally, the similar energy region (SER) technique provides a high-possibility region to the system, allowing the client robot to locate itself in a short time.

This chapter conceptually addresses outward internationalisation of small and medium sized enterprises (SMEs) from the emerging markets (EMs) by focussing on the role of prior contract manufacturing relationships with a developed market multinational enterprise (DMNE). The internationalisation of SMEs originating from EMs is a rather under-researched area and the role of prior contract manufacturing experience specifically has not been addressed in prior studies. Based on a literature review, the authors identified four capabilities developed by EM SMEs during their contract manufacturing relationships with DMNE(s) that potentially help in later outward internationalisation. The authors incorporate some insights from dynamic capabilities theory, and develop propositions addressing the role of relational capital, human capital, manufacturing productivity capabilities and product innovation capabilities in this specific context. Despite being conceptual in nature, this chapter is one of the first to explicitly highlight the role of these specific capabilities developed during contract manufacturing relationship for outward internationalisation, setting bases for future studies to further empirically investigate them in different contexts.

The purpose of this paper is to investigate the relationship among intellectual capital (IC), financial capital (FC), firm value (V), and value creation (VC) in different business cycles (BC) for the conduct of strategic management that will maintain stable values and further increase V.

This research cites ICs as “other information” to combine ICs and the Ohlson model. Information provided by various capitals is validated by multiple regression analysis. Multi-group analysis is performed to test whether the coefficient is moderated by BC.

Results indicate the significant information of ICs and FC, and the contingency perspective of BC. The value relevance of ICs is moderated by BC. Prosperity has more explanatory capacities, and recession ICs yield more incremental information.

VC is influenced by both ICs and FC. Besides, the macroeconomic situation should also be considered in strategic management and VC management.

In addition to ICs and FC, the macroeconomic situation must be taken into account when conducting strategic management, valuation management, investment decision, or industrial policy.

Results indicate a contingency of BC, which can be a reference for enterprises to create higher V, for investors to make appropriate investment, as well as for governments to formulate sound industrial policies.

This paper applies BC to explore the value relevance of ICs and FC, leverages two models to represent V and VC, and cites complete four aspects of IC as “other information” to combine ICs and Ohlson model.

Chief executive officer (CEO) ability may have an effect on various corporate reporting decisions, and consequently, the CEO ability is subject to scrutiny by regulatory reviewers. However, theoretical literature provides mixed evidence on how the CEO ability affects the regulatory review risk. Thus, this study aims to empirically examine the effect of CEO ability on regulatory review risk.

To measure CEO ability, this study uses the CEO ability-score developed by Demerjian et al. (2012). Further, to measure regulatory review risk, the study uses the probability of receiving a comment letter from the Securities and Exchange Organization of Iran.

This study finds that the relationship between CEO ability and regulatory review risk is generally negative and statistically significant but not economically significant, i.e. the relationship is very small. In this regard, the study shows that the relationship is negative and also statistically and economically significant for firms with low levels of agency conflicts and high levels of corporate governance quality; and is positive and also statistically and economically significant for firms with high levels of agency conflicts and low levels of corporate governance quality. In addition, while the study finds no evidence that the regulatory reviewers’ workload compression influences the general relationship between CEO ability and regulatory review risk, it documents that low (high) regulatory reviewers’ workload compression weakens (strengthens) both the relationships stated above.

Collectively, the results suggest that the agency conflicts/corporate governance quality and regulatory reviewers’ workload compression are important factors in the analysis of the relationship between the CEO ability and regulatory review risk. The results offer insights into the opposing theoretical viewpoints about the relationship between CEO ability and regulatory review risk. Thus, the results will be of interest to boards of directors and other stakeholders involved in the regulatory review process.

This study investigates the association between managerial ability and earnings quality in firms listed on the Iraq Stock Exchange and how the emergence of the Islamic State of Iraq and Syria (ISIS) influences the association.

This study uses a sample of firms listed on the Iraq Stock Exchange over the period 2012–2018. Managerial ability is quantified using data envelopment analysis, and earnings quality is measured by earnings restatement, earnings persistence, accruals quality and earnings response coefficient. Panel regression analysis is used to examine the research hypotheses.

The findings indicate that managerial ability positively affects earnings quality of Iraqi firms and that ISIS weakens the relationship between managerial ability and earnings quality. These findings are robust to the alternative measures of managerial ability, as well as to various approaches used to address endogeneity including propensity-score matching and a difference-in-differences analysis.

This study provides insight into the impact of managerial ability on earnings quality in an under-studied emerging market. Furthermore, this study broadens the existing literature about the financial consequences of a modern terrorist group, ISIS.

This paper aims at the problem of attaching the data of doctors, patients and the real-time sensor data of the exoskeleton to the cloud in intelligent rehabilitation applications. This study designed the exoskeleton cloud-brain platform and validated its safety assessment.

According to the dimension of data and the transmission speed, this paper implements a three-layer cloud-brain platform of exoskeleton based on Alibaba Cloud's Lambda-like architecture. At the same time, given the human–machine safety status detection problem of the exoskeleton, this paper built a personalized machine-learning safety detection module for users with the multi-dimensional sensor data cloned by the cloud-brain platform. This module includes an abnormality detection model, prediction model and state classification model of the human–machine state.

These functions of the exoskeleton cloud-brain and the algorithms based on it were validated by the experiments, they meet the needs of use.

This thesis innovatively proposes a cloud-brain platform for exoskeletons, beginning the digitalization and intelligence of the exoskeletal rehabilitation process and laying the foundation for future intelligent assistance systems.