• Journal of the Ergonomics Society of Korea
• /
• v.32 no.5
• /
• pp.413-420
• /
• 2013

Objective: The objective of this study was to compare one-hand and two-hands lowering activity in terms of biomechanical stress for the range of lowering heights from knuckle height to 10cm above floor level. Background: Even though two-hands lifting/lowering activity of manual materials handling tasks are prevalent at the industrial site, many manual materials handling tasks which require the worker to perform one-hand lifting/lowering are also very common at the industrial site and forestry and farming. Method: Eight male subjects were asked to perform lowering tasks using both a one-handed as well as a two-handed lowering technique. Trunk muscle electromyographic activity was recorded while the subjects performed the lowering tasks. This information was used as input to an EMG-assisted free-dynamic biomechanical model that predicted spinal loading in three dimensions. Results: It was shown that for the left-hand lowering tasks, the values of moment, lateral shear force, A-P shear force, and compressive force were increased by the average 6%, as the workload was increased twice from 7.5kg to 15kg. For the right-hand lowering task, these were increased by the average 17%. For the two-hands lowering tasks, these were increased by the average 14%. Conclusion: Even though the effect of workload on the biomechanical stress for both one-hand and two-hands lowering tasks is not so significant for the workload less than 15kg, it can be claimed that the biomechanical stress for one-hand lowering is greater than for two-hands lowering tasks. Therefore, it can be concluded that asymmetrical lowering posture would give greater influence on the biomechanical stress than the workload effect for one-hand lowering activity. Application: The result of this study may be used to provide guidelines of recommended safe weights for tasks involved in one-hand lowering activity.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.4
• /
• pp.293-300
• /
• 2016

The Voice recognition is one of convenient methods to communicate between human and robots. This study proposes a speech recognition method using speech recognizers based on Hidden Markov Model (HMM) with a combination of techniques to enhance a biped robot control. In the past, Artificial Neural Networks (ANN) and Dynamic Time Wrapping (DTW) were used, however, currently they are less commonly applied to speech recognition systems. This Research confirms that the HMM, an accepted high-performance technique, can be successfully employed to model speech signals. High recognition accuracy can be obtained by using HMMs. Apart from speech modeling techniques, multiple feature extraction methods have been studied to find speech stresses caused by emotions and the environment to improve speech recognition rates. The procedure consisted of 2 parts: one is recognizing robot commands using multiple HMM recognizers, and the other is sending recognized commands to control a robot. In this paper, a practical voice recognition system which can recognize a lot of task commands is proposed. The proposed system consists of a general purpose microprocessor and a useful voice recognition processor which can recognize a limited number of voice patterns. By simulation and experiment, it was illustrated the reliability of voice recognition rates for application of the manufacturing process.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.2
• /
• pp.131-137
• /
• 2006

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforcement learning having delayed reward ability and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforcement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.1 no.2
• /
• pp.159-168
• /
• 1997

This paper presents a mechanism which supplies tasks with fast turn-around time on real-time multimedia environments. Tasks are classified into periodic and aperiodic tasks according to their executing period, and the types of them are classified into three groups : critical tasks, essential tasks and common tasks by the degree of its urgency. In the case of periodic tasks, we defer the execution of it within the extent to keep the deadline as long as possible and serve the aperiodic tasks, and provide aperiodic tasks with fast turn-around time. Changing the priority of each task is allowed within the same type and it is scheduled by using the dynamic priority. The emergency tasks are executed within deadline in any circumstances, and the least laxity one is served first when many real-time tasks are waiting for execution. The result of simulation shows that the proposed mechanism is better than the EDZL, known as suboptimal in multiprocessor systems, in the point of rum-around time.

• Journal of KIISE:Databases
• /
• v.28 no.3
• /
• pp.430-448
• /
• 2001

In navigation system, a primary task is to compute the minimum cost route from the current location to the destination. One of major problems for navigation systems is that a significant amount of computation time is required when the digital road map is large. Since navigation systems are real time systems, it is critical that the path be computed while satisfying a time constraint. In this paper, we have developed a HiTi(Hierarchical MulTi) graph model for hierarchically structuring large digital road maps to speedup the minimum cost path computation. We propose a new shortest path algorithm named SPAH, which utilizes HiTi graph model of a digital road map for its computation. We prove that the shortest path computed by SPAH is the optimal. Our performance analysis of SPAH also showed that it significantly reduces the computation time over exiting methods. We present an in-depth experimental analysis of HiTi graph method by comparing it with other similar works.

• The KIPS Transactions:PartA
• /
• v.8A no.4
• /
• pp.429-438
• /
• 2001

GPS (Global Positioning System) is the most ideal navigation system which can be used on the earth irrespective of time and weather conditions. GPS has been used for various applications such as construction, survey, environment, communication, intelligent vehicles and airplanes and the needs of GPS are increasing in these days. This paper deals with the design and implementation of the RTOS (Real-Time Operating System) for a GPS navigation computer in the GPS/INS integrated navigation system. The RTOS provides the optimal environment for execution and the base platform to develop GPS application programs. The key facilities supplied by the RTOS developed in this paper are priority-based preemptive scheduling policy, dynamic memory management, intelligent interrupt handling, timers and IPC, etc. We also verify the correct operations of all application tasks of the GPS navigation computer on the RTOS and evaluate the performance by measuring the overhead of using the RTOS services.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.10B
• /
• pp.899-915
• /
• 2003

Delivering rich and continuous media contents robustly over a wide range of network conditions of the wired/wireless Internet is a highly challenging task. To address this challenges, the continuous media applications at the edge of network has become more and more adaptive while the best-effort Internet is slowly progressing towards improved networking services. That is, the role of network adaptive media delivery, which dynamically links the quality demand of application contents to the underlying networking services, has become more crucial. In this paper, we will first review the required network adaptation functionalities seen from the application side: congestion control / rate control, error control, and synchronization / adaptive playout. Then, we start the coverage of networking support issues that helps the realization of network adaptive media streaming - from network support and protocol support toward consolidated support via middleware. Finally, we propose a dynamic network adaptation framework that efficiently leverages its awareness of both media application (including contents) and underlying networking support.

• The Journal of Korean Physical Therapy
• /
• v.29 no.5
• /
• pp.246-254
• /
• 2017

Purpose: Previous studies have reported that action observation training has beneficial effects on enhancing the motor task, such as balance and gait functions. On the other hand, there have been few studies combined with action observation training and auditory feedback. The purpose of this study was to determine the effects of action observation training with auditory feedback on the gait function in stroke patients with hemiparesis Methods: A total of 24 inpatients with post-stroke hemiparesis were assigned randomly to either an experimental group 1 (EG 1, n=8), experiment group 2 (EG 2, n=8), control group (CG, n=8, EG 1). The EG 2 and CG watched video clip demonstrating three functional walking tasks with auditory feedback, without auditory feedback, and showing a landscape image, respectively. The exercise program consisted of 30 minutes, five times a week, for four weeks. The participants were measured to 10MWT (10 m walk test), 6MWT (6 minutes walking distance test), TUG (timed up and go test), DGI (dynamic gait index), time and steps of F8WT (figure-of-8 walk test). Results: In the intra-group comparison after the intervention, EG 1 and EG 2 showed a significantly different gait function (10MWT, 6MWT, DGI, TUG, F8WT) (p<0.05). In the inter-group comparison after intervention, EG 1 showed significant improvements in the entire gait parameters and EG 2 only showed significant improvement in DGI and TUG compared to CG (p<0.05). Conclusion: These findings show that action observation training with auditory feedback may be used beneficially for improving the gait function of stroke patients with hemiparesis.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.5
• /
• pp.2044-2062
• /
• 2018

Information theory progression along with the advancements being made in the field of Vehicular Ad hoc NETworks (VANETs) supports the use of coding-aware opportunistic routing for efficient data forwarding. In this work, we propose and investigate an adaptive coding-aware routing scheme in a specific VANET scenario known as a vehicular platoon. Availability of coding opportunities may vary with time and therefore, the accurate identification of available coding opportunities at a specific time is a quite challenging task in the highly dynamic scenario of VANETs. In the proposed approach, while estimating the topology of the network at any time instance, a forwarding vehicle contemplates the composition of multiple unicast data flows to encode the correct data packets that can be decoded successfully at destinations. The results obtained by using OMNeT++ simulator reveal that higher throughput can be achieved with minimum possible packet transmissions through the proposed adaptive coding-aware routing approach. In addition, the proposed adaptive scheme outperforms static transmissions of the encoded packets in terms of coding gain, transmission percentage, and encoded packet transmission. To the best of our knowledge, the use of coding-aware opportunistic routing has not been exploited extensively in available literature with reference to its implications in VANETs.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.5
• /
• pp.591-597
• /
• 2007

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new polygon based Q-learning algorithm and distributed genetic algorithms are proposed for behavior learning and evolution of collective autonomous mobile robots. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.