Policy support for own-brand innovation in China’s auto industry: panacea or placebo?

2017 ◽  
Vol 11 (1) ◽  
pp. 107-122 ◽  
Author(s):  
Dongjin Yang ◽  
Chin Tachia ◽  
Liu Ren-huai ◽  
Zuowei Yao
Keyword(s):  
Joint Ventures ◽  
Auto Industry ◽  
Economic Transformation ◽  
Policy Support ◽  
Brand Strategy ◽  
Data Set ◽  
Content Type ◽  
Governmental Policy ◽  
Public Data ◽  
Context Specific

Purpose China has become the world’s largest vehicle market, because of the strong governmental support to boost car sales and particularly because of the establishment of joint ventures between state-owned enterprises and world-class automakers. However, because many Sino-foreign joint ventures have performed unsatisfactorily in terms of creating indigenous brands, the purpose of this paper is to explore the cause-and-effect relationships among governmental policy support, Sino-foreign joint ventures and own-brand innovation in China’s passenger-car industry. Design/methodology/approach After briefly introducing the development history of the Chinese auto industry and reviewing relevant literature, first, the analytic hierarchy process method is used to create a unique, context-specific equation to measure the degree of policy support in China. This paper then uses the hierarchical multiple regression method to process the 2014 public data set. Findings The findings show that the degree of policy support increases the preference of the firms for producing foreign-brand cars, while such a relationship is fully mediated by the establishment of Sino-foreign joint ventures. Research limitations/implications The research brings greater and deeper insights into the interplay among governmental policy, the conduct of own-brand strategy and international joint ventures in China’s auto market, showing that policy support may not always be beneficial, but sometimes be detrimental to indigenous innovation. Originality/value This paper can be seen as an exciting step that adds to a better understanding of the role of political support in shaping the strategic choices of firms in terms of brand innovation in the Chinese automobile industry. The proposed novel, context-specific approach for evaluating the degree of policy support embodies the distinctive institutional complexity and intricate social network embedded in the local car market during the period of China’s socio-economic transformation – an approach that is original in this field.

Fast and accurate detection of surface defect based on improved YOLOv4

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiawei Lian ◽  
Junhong He ◽  
Yun Niu ◽  
Tianze Wang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Surface Defect ◽  
Steel Ingot ◽  
Industrial Applications ◽  
Data Sets ◽  
Data Set ◽  
Processing Technologies ◽  
Content Type ◽  
Public Data

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

A progressive online external parameter calibration and initialization method for stereo-IMU system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanwu Zhai ◽  
Haibo Feng ◽  
Yili Fu
Keyword(s):  
Local Minimum ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
External Parameter ◽  
Parameter Calibration ◽  
Data Set ◽  
Content Type ◽  
Inertial Parameters ◽  
Public Data ◽  
Previous Step

Purpose This paper aims to present a pipeline to progressively deal with the online external parameter calibration and estimator initialization of the Stereo-inertial measurement unit (IMU) system, which does not require any prior information and is suitable for system initialization in a variety of environments. Design/methodology/approach Before calibration and initialization, a modified stereo tracking method is adopted to obtain a motion pose, which provides prerequisites for the next three steps. Firstly, the authors align the pose obtained with the IMU measurements and linearly calculate the rough external parameters and gravity vector to provide initial values for the next optimization. Secondly, the authors fix the pose obtained by the vision and restore the external and inertial parameters of the system by optimizing the pre-integration of the IMU. Thirdly, the result of the previous step is used to perform visual-inertial joint optimization to further refine the external and inertial parameters. Findings The results of public data set experiments and actual experiments show that this method has better accuracy and robustness compared with the state of-the-art. Originality/value This method improves the accuracy of external parameters calibration and initialization and prevents the system from falling into a local minimum. Different from the traditional method of solving inertial navigation parameters separately, in this paper, all inertial navigation parameters are solved at one time, and the results of the previous step are used as the seed for the next optimization, and gradually solve the external inertial navigation parameters from coarse to fine, which avoids falling into a local minimum, reduces the number of iterations during optimization and improves the efficiency of the system.

An end-to-End deep context gate convolutional visual odometry system based on lightweight attention mechanism

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yan Xu ◽  
Hong Qin ◽  
Jiani Huang ◽  
Yanyun Wang
Keyword(s):  
Visual Odometry ◽  
Attention Mechanism ◽  
Learning Ability ◽  
Global Features ◽  
Data Set ◽  
Content Type ◽  
Generalization Ability ◽  
The Public ◽  
Public Data ◽  
End To End

Purpose Conventional learning-based visual odometry (VO) systems usually use convolutional neural networks (CNN) to extract features, where some important context-related and attention-holding global features might be ignored. Without essential global features, VO system will be sensitive to various environmental perturbations. The purpose of this paper is to design a novel learning-based framework that aims to improve accuracy of learning-based VO without decreasing the generalization ability. Design/methodology/approach Instead of CNN, a context-gated convolution is adopted to build an end-to-end learning framework, which enables convolutional layers that dynamically capture representative local patterns and composes local features of interest under the guidance of global context. In addition, an attention mechanism module is introduced to further improve learning ability and enhance robustness and generalization ability of the VO system. Findings The proposed system is evaluated on the public data set KITTI and the self-collected data sets of our college building, where it shows competitive performance compared with some classical and state-of-the-art learning-based methods. Quantitative experimental results on the public data set KITTI show that compared with CNN-based VO methods, the average translational error and rotational error of all the test sequences are reduced by 45.63% and 37.22%, respectively. Originality/value The main contribution of this paper is that an end-to-end deep context gate convolutional VO system based on lightweight attention mechanism is proposed, which effectively improves the accuracy compared with other learning-based methods.

scholarly journals A data set of aerial imagery from robotics simulator for map-based localization systems benchmark

2019 ◽  
Vol 8 (3) ◽  
pp. 177-186
Author(s):  
Rokas Jurevičius ◽  
Virginijus Marcinkevičius
Keyword(s):  
High Altitude ◽  
Aerial Imagery ◽  
Data Sets ◽  
Accuracy Evaluation ◽  
Data Set ◽  
Content Type ◽  
Localization System ◽  
Starting Point ◽  
Public Data ◽  
Research Studies

Purpose The purpose of this paper is to present a new data set of aerial imagery from robotics simulator (AIR). AIR data set aims to provide a starting point for localization system development and to become a typical benchmark for accuracy comparison of map-based localization algorithms, visual odometry and SLAM for high-altitude flights. Design/methodology/approach The presented data set contains over 100,000 aerial images captured from Gazebo robotics simulator using orthophoto maps as a ground plane. Flights with three different trajectories are performed on maps from urban and forest environment at different altitudes, totaling over 33 kilometers of flight distance. Findings The review of previous research studies show that the presented data set is the largest currently available public data set with downward facing camera imagery. Originality/value This paper presents the problem of missing publicly available data sets for high-altitude (100‒3,000 meters) UAV flights; the current state-of-the-art research studies performed to develop map-based localization system for UAVs depend on real-life test flights and custom-simulated data sets for accuracy evaluation of the algorithms. The presented new data set solves this problem and aims to help the researchers to improve and benchmark new algorithms for high-altitude flights.

The double-edged sword of cultural distance in international alliances

2017 ◽  
Vol 24 (1) ◽  
pp. 33-54 ◽  
Author(s):  
Robin Pesch ◽  
Ricarda B. Bouncken
Keyword(s):  
Joint Ventures ◽  
Manufacturing Industry ◽  
Cultural Distance ◽  
Equation Modeling ◽  
Negative Effects ◽  
Data Set ◽  
Content Type ◽  
Positive Effects ◽  
International Alliances ◽  
Perceived Cultural Distance

Purpose While previous studies have primarily assumed dysfunctional effects of cultural distance in joint ventures and M&A, this paper elucidates from a positive organizational scholarship perspective how perceived cultural distance can advance firms’ new product development within non-equity alliances. The purpose of this paper is to explain how perceived cultural distance stimulates task discourse that supports alliance partners’ employees in recognizing and applying culture-related differences as complementary problem-solving potentials. Due to a lower integration level in non-equity alliances compared to joint ventures or M&A, this paper assumes that the positive effects outweigh the negative effects of cultural distance. Design/methodology/approach This study applies structural equation modeling to test the hypothesized effects on a sample of 246 international alliances in the manufacturing industry. Findings The analysis mainly supports the hypothesized model and unravels how positive effects can emerge from perceived cultural distance. Practical implications The findings provide managerial implications. Alliance managers should note that cultural distance can have positive and negative effects, and thus it is not a barrier per se in alliances. Firms can benefit from cultural distance if they are able to leverage culture-specific complementarities through task discourse among partners in alliances. Originality/value The manuscript uses a unique data set of 246 international alliances from the global manufacturing industry. The manuscript has not been published elsewhere.

Exhibitions as sub-brands: an exploratory study

2014 ◽  
Vol 4 (1/2) ◽  
pp. 45-66 ◽  
Author(s):  
Ruth Rentschler ◽  
Kerrie Bridson ◽  
Jody Evans
Keyword(s):  
Art Museum ◽  
Art Museums ◽  
Comparative Case Study ◽  
Brand Strategy ◽  
Community Art ◽  
Data Set ◽  
Content Type ◽  
Non Profit ◽  
Key Characteristics ◽  
Institutional Learning

Purpose – The purpose of this paper is to explore the adoption of major exhibitions, often called blockbusters, as a sub-branding strategy for art museums. Focusing the experience around one location but drawing on a wide data set for comparative purposes, the authors examine the blockbuster phenomenon as exhibition packages sourced from international institutions, based on an artist or collection of quality and significance. The authors answer the questions: what drives an art museum to adopt an exhibition sub-brand strategy that sees exhibitions become blockbusters? What are the characteristics of the blockbuster sub-brand? Design/methodology/approach – Using extant literature, interviews and content analysis in a comparative case study format, this paper has three aims: first, to embed exhibitions within the marketing and branding literature; second, to identify the drivers of a blockbuster strategy; and third, to explore the key characteristics of blockbuster exhibitions. Findings – The authors present a theoretical model of major exhibitions as a sub-brand. The drivers identified include the entrepreneurial characteristics of pro-activeness, innovation and risk-taking, while the four key characteristics of the blockbuster are celebrity; spectacle; inclusivity; and authenticity. Practical implications – These exhibitions are used to augment a host art museum’s own collection for its stakeholders and differentiate it in the wider cultural marketplace. While art museum curators seek to develop quality exhibitions, sometimes they become blockbusters. While blockbusters are a household word, the terms is contested and the authors know little about them from a marketing perspective. Social implications – Art museums are non-profit, social organisations that serve the community. Art museums therefore meet the needs of multiple stakeholders in a political environment with competing interests. The study draws on the experiences of a major regional art museum, examining the characteristics of exhibition sub-brands and the paradox of the sub-brand being used to differentiate the art museum. This paper fills a gap in both the arts marketing and broader marketing literature. Originality/value – The use of the identified characteristics develops theory where the literature has been silent on the blockbuster sub-brand from a marketing perspective. It provides an exemplar for institutional learning on how to initiate and manage quality by popular exhibition strategies.

An averaging approach to individual time series employing econometric models: a case study on NN5 ATM transactions data

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michele Cedolin ◽  
Mujde Erol Genevois
Keyword(s):  
Time Series ◽  
Financial Institutions ◽  
Moving Average ◽  
Demand Forecasting ◽  
Computational Time ◽  
Data Set ◽  
Content Type ◽  
Atm Network ◽  
Public Data ◽  
Cash Demand

PurposeThe research objective is to increase the computational efficiency of the automated teller machine (ATM) cash demand forecasting problem. It proposes a practical decision-making process that uses aggregated time series of a bank's ATM network. The purpose is to decrease ATM numbers that will be forecasted by individual models, by finding the machines’ cluster where the forecasting results of the aggregated series are appropriate to use.Design/methodology/approachA comparative statistical forecasting approach is proposed in order to reduce the calculation complexity of an ATM network by using the NN5 competition data set. Integrated autoregressive moving average (ARIMA) and its seasonal version SARIMA are fitted to each time series. Then, averaged time series are introduced to simplify the forecasting process carried out for each ATM. The ATMs that are forecastable with the averaged series are identified by calculating the forecasting accuracy change in each machine.FindingsThe proposed approach is evaluated by different error metrics and is compared to the literature findings. The results show that the ATMs that have tolerable accuracy loss may be considered as a cluster and can be forecasted with a single model based on the aggregated series.Research limitations/implicationsThe research is based on the public data set. Financial institutions do not prefer to share their ATM transactions data, therefore accessible data are limited.Practical implicationsThe proposed practical approach will be beneficial for financial institutions to use, that hold an excessive number of ATMs because it reduces the computational time and resources allocated for the forecasting process.Originality/valueThis study offers an effective simplified methodology to the challenging cash demand forecasting process by introducing an aggregated time series approach.

An adaptive scheme for degradation suppression in Lidar based SLAM

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Zhang Rui Huang
Keyword(s):  
Environmental Changes ◽  
Field Tests ◽  
Distortion Correction ◽  
Dynamic Threshold ◽  
Data Set ◽  
Adaptive Scheme ◽  
Content Type ◽  
Localization Accuracy ◽  
Public Data ◽  
The Impact

Purpose With the booming development of computer, optical and sensing technologies and cybernetics, the technical research in unmanned vehicle has been advanced to a new era. This trend arouses great interest in simultaneous localization and mapping (SLAM). Especially, light detection and ranging (Lidar)-based SLAM system has the characteristics of high measuring accuracy and insensitivity to illumination conditions, which has been widely used in industry. However, SLAM has some intractable problems, including degradation under less structured or uncontrived environment. To solve this problem, this paper aims to propose an adaptive scheme with dynamic threshold to mitigate degradation. Design/methodology/approach We propose an adaptive strategy with a dynamic module is proposed to overcome degradation of point cloud. Besides, a distortion correction process is presented in the local map to reduce the impact of noise in the iterative optimization process. Our solution ensures adaptability to environmental changes. Findings Experimental results on both public data set and field tests demonstrated that the algorithm is robust and self-adaptive, which achieved higher localization accuracy and lower mapping error compared with existing methods. Originality/value Unlike other popular algorithms, we do not rely on multi-sensor fusion to improve the localization accuracy. Instead, the pure Lidar-based method with dynamic threshold and distortion correction module indeed improved the accuracy and robustness in localization results.

Critical management issues in China’s socio-economic transformation

2017 ◽  
Vol 11 (1) ◽  
pp. 12-18 ◽  
Author(s):  
Chin Tachia ◽  
Liu Ren-huai
Keyword(s):  
Business Environment ◽  
Economic Transformation ◽  
Chinese Government ◽  
Business Practices ◽  
Chinese Firms ◽  
Content Type ◽  
Wide Range ◽  
Critical Management ◽  
Context Specific ◽  
Management Issues

Purpose This paper aims to introduce ten studies included in this themed issue that illustrate from multifaceted angles some critical management issues and context-specific challenges on strategy and innovation facing the State and enterprises during China’s socio-economic transition. Instead of focusing on topics from the literature, this special issue (SI) pays more attention to characterising unique Chinese business practices in the transformation period. Design/methodology/approach The ten manuscripts were selected for this SI so that readers can compare how scholars used different research designs and multiple analytical and statistical approaches to draw conclusions. Findings These studies involve a wide range of aspects, as well as diversified perspectives demonstrating some critical management issues and context-specific phenomenon associated with the development of strategy and innovation in contemporary China. The results show that while pollution-related issues have had a damaging effect on China’s business environment, the Chinese government has to enact and enforce stricter environmental laws to promote technology innovation in a healthier manner; moreover, Chinese firms should pay greater attention to the trade-off between the increasing of resource consumption for growth and the reducing of energy use for the sake of the planet. In response to the grand innovation challenges Chinese manufacturing is confronted with, these papers suggest that policy support may not always be beneficial but sometimes detrimental to independent innovation, and that Chinese manufacturers may ultimately get access to the key and core technology of forerunners by forming a R&D strategic alliance in periphery knowledge/technology first. Overall, the outcomes of these studies provide a bigger picture and intriguing implications that may inspire practitioners, policymakers and academics to further ponder relevant issues in a more comprehensive way. Originality/value All ten studies based on original data were not reported elsewhere and demonstrated results that have not been addressed in prior research. This paper enriches one’s understanding of how Chinese firms have been deliberately seeking their own distinctive trajectories of developing strategy and innovation dissimilar to those of advanced economy companies, given the peculiar cultural background and institutional systems. Future research trends and opportunities are also outlined.

Predicting future AI failures from historic examples

foresight ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 138-152 ◽  
Author(s):  
Roman V. Yampolskiy
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Design Methodology ◽  
Security System ◽  
Moderate Level ◽  
Catastrophic Event ◽  
Data Set ◽  
Content Type ◽  
Public Data ◽  
Ai Safety

Purpose The purpose of this paper is to explain to readers how intelligent systems can fail and how artificial intelligence (AI) safety is different from cybersecurity. The goal of cybersecurity is to reduce the number of successful attacks on the system; the goal of AI Safety is to make sure zero attacks succeed in bypassing the safety mechanisms. Unfortunately, such a level of performance is unachievable. Every security system will eventually fail; there is no such thing as a 100 per cent secure system. Design/methodology/approach AI Safety can be improved based on ideas developed by cybersecurity experts. For narrow AI Safety, failures are at the same, moderate level of criticality as in cybersecurity; however, for general AI, failures have a fundamentally different impact. A single failure of a superintelligent system may cause a catastrophic event without a chance for recovery. Findings In this paper, the authors present and analyze reported failures of artificially intelligent systems and extrapolate our analysis to future AIs. The authors suggest that both the frequency and the seriousness of future AI failures will steadily increase. Originality/value This is a first attempt to assemble a public data set of AI failures and is extremely valuable to AI Safety researchers.

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