• Knowledge Management Research
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• v.18 no.4
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• pp.31-62
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• 2017

It found that employee silence negatively impacts both organizations and their employees as shown in findings from many studies and recently there has been a growing interest in it. Silence is described as intentionally withholding job-related ideas, information, concerns, and opinions. Employee silence may decrease organizational change and innovation and reduce employee learning motivation and knowledge sharing engagement as well. The purpose of this study is to examine the relationships among silence motivations, perceived silence climate, and employee silence; the relationships among employee silence, learning inertia and knowledge sharing disengagement; the mediating role of employee silence between antecedents of employee silence and consequences additionally. The results that analyzed using data from 225 employees in 42 organizations are as follows. First, the impact of silence motivation and perceived silence climate on employee silence are positively significant. Second, the influence of defensive silence motivation on the acquiescent and relational silence motivation is positively significant. Third, the influence of employee silence on learning inertia and knowledge sharing disengagement is positively significant. Forth, employee silence mediates the relationship between silence motivation and perceived silence climate and learning inertia and knowledge sharing disengagement. These results suggest that employee silence is another strong expression and message for organizations to try to establish a learning organization from the perspective of knowledge management.

• Knowledge Management Research
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• v.16 no.2
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• pp.249-278
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• 2015

Employees' knowledge integration capabilities and innovative behavior are still of crucial importance in the effective knowledge management. Recently researchers and practitioners are interested in both the potential benefits of unlearning and the negative aspects of learning inertia. The purpose of this study is to examine the relationships among learning inertia, unlearning, knowledge integration capabilities(knowledge exploitation and knowledge exploration) and innovative behavior. The results of analysis show that learning inertia is employees' psychological obstacle factor affecting knowledge integration capabilities and unlearning, that unlearning of employees is a key factor affecting knowledge integration capabilities, and that knowledge integration capabilities are driving forces leading to innovative behaviors of employees. For theoretical and practical implications, the research presents the grounds for arguments that knowledge integration capabilities are employees' dynamic capabilities from the knowledge management perspective, that unlearning is a driving force of employees' positive behaviors, and that organizations trying to perform the dynamic knowledge management need to identify the causes of employees' psychological resistance to learning. Limitations arisen in the course of the research and suggestions for future research directions are also discussed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.17-24
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• 1994

This paper is a study on Korean word recognition and suggest the method that stabilizes the state-transition in the HMM by applying the `inertia' to the feature vector sequences. In order to reduce the quantized distortion considering probability distribution of input vectors, we used SOFM, an unsupervised learning method, as a vector quantizer, By applying inertia to the feature vector sequences, the overlapping of probability distributions for the response path of each word on the self organizing feature map can be reduced and the state-transition in the Hmm can be Stabilized. In order to evaluate the performance of the method, we carried out experiments for 50 DDD area names. The results showed that applying inertia to the feature vector sequence improved the recognition rate by 7.4% and can make more HMMs available without reducing the recognition rate for the SOFM having the fixed number of neuron.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.76-81
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• 2004

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.

• Journal of Mechanical Science and Technology
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• v.18 no.8
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• pp.1388-1400
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• 2004

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.723-729
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• 2001

Electro-hydraulic shift control of a vehicle automatic transmission has been predominantly carried out via an open-loop control based on numerous time-consuming calibrations. Despite remarkable success in practice, the variations of system characteristics inevitably deteriorate the performance of the tuned open-loop controller. As a result, the controller parameters need to be continuously updated in order to maintain satisfactory shift quality. This paper presents a self-learning algorithm for automatic transmission shift control in a construction vehicle during inertia phase. First, an observer reconstructs the turbine acceleration signal (impossible to measure in a construction vehicle) from the readily accessible turbine speed measurement. Then, a control algorithm based on a quadratic function of the turbine acceleration is shown to guarantee the asymptotic convergence (within a specified target bound) of the error between the actual and the desired turbine accelerations. A Lyapunov argument plays a crucial role in deriving adaptive laws for control parameters. The simulation and hardware-in-the-loop simulation (HILS) studies show that the proposed algorithm actually delivers the promise of satisfactory performance despite the system characteristics variations and uncertainties.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.421-424
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• 1990

An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic systems is presented. In the learning control structure, tracking and feedforward input converge globally and asymptotically as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of length of trajectories, it may be achieved with only system trajectories of small duration. In addition, these learning control schemes are expected to be effectively applicable to time-varying parametric systems as well as time-invariant systems, for the parameter estimation is performed at each fixed time along the iteration. Finally, no usage of acceleration signal and no in version of estimated inertia matrix in the parameter estimator makes these learning control schemes more feasible.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.4
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• pp.427-438
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• 1991

An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic system is preented. In the learning control structure, the control input converges globally and asymtotically to the desired input as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of the time-duration of trajectories, it may be achieved with system trajectories with small duration. In addition, the proposd learning control schemes are applicable to time-varying parametric systems as well as time-invariant systems, because the parameter estimation is performed at each fixed time along the iteration. In the parameter estimator, the acceleration information as well as the inversion of estimated inertia matrix are not used at all, which makes the proposed learning control schemes more feasible.