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Dr. Kittipong Ekkachai
National Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA)

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Research Keywords & Expertise

0 Embedded and Real-Time Systems
0 MR damper
0 Meta-heuristic optimization algorithm
0 Robotic and Automation
0 Biomechanical analysis

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Journal article
Published: 01 April 2021 in Applied Sciences
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The textiles and garment industry plays an important role in Thailand’s economic growth, despite facing competition in product quality and rising production costs. Meeting diverse consumer needs and satisfaction has become increasingly difficult, as environmental issues become a major concern for firms internationally. Entrepreneurs require sophisticated strategic management techniques to maintain organizational productivity. Growing industries generate material losses, while negatively impacting the environment. Companies may account for their waste, but in reality, actual productivity is much lower, since hidden wastes are mostly unaccounted for and unquantified. A key barrier to reducing waste is that potential cost savings by revising waste management processes are not calculated. To solve this problem, material flow cost accounting (MFCA) was introduced to reduce negative product costs in a ladies’ lingerie company by identifying and evaluating the quantity and cost of concealed material waste. An effective meta-heuristic called the Two-Dimensional Cutting Stock Problem—Tabu Search algorithm (2DCSP-TS) was then proposed based on the idea of finding a layout that minimized a bin length. The multi-phase arrangement strategy embedded in it can obtain near-optimal conditions for solving realistic-sized problems. To illustrate the effectiveness of the proposed methods, numerical experimental results were compared with those of the current practice. From the numerical experiments, it was found that the proposed technique is an efficient method for reducing negative product costs.

ACS Style

Darat Dechampai; Samerjit Homrossukon; Wuthichai Wongthatsanekorn; Kittipong Ekkachai. Applying Material Flow Cost Accounting and Two-Dimensional, Irregularly Shaped Cutting Stock Problems in the Lingerie Manufacturing Industry. Applied Sciences 2021, 11, 3142 .

AMA Style

Darat Dechampai, Samerjit Homrossukon, Wuthichai Wongthatsanekorn, Kittipong Ekkachai. Applying Material Flow Cost Accounting and Two-Dimensional, Irregularly Shaped Cutting Stock Problems in the Lingerie Manufacturing Industry. Applied Sciences. 2021; 11 (7):3142.

Chicago/Turabian Style

Darat Dechampai; Samerjit Homrossukon; Wuthichai Wongthatsanekorn; Kittipong Ekkachai. 2021. "Applying Material Flow Cost Accounting and Two-Dimensional, Irregularly Shaped Cutting Stock Problems in the Lingerie Manufacturing Industry." Applied Sciences 11, no. 7: 3142.

Original research article
Published: 26 November 2020 in Frontiers in Neurorobotics
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In this study, we investigated a control algorithm for a semi-active prosthetic knee based on reinforcement learning (RL). Model-free reinforcement Q-learning control with a reward shaping function was proposed as the voltage controller of a magnetorheological damper based on the prosthetic knee. The reward function was designed as a function of the performance index that accounts for the trajectory of the subject-specific knee angle. We compared our proposed reward function to a conventional single reward function under the same random initialization of a Q-matrix. We trained this control algorithm to adapt to several walking speed datasets under one control policy and subsequently compared its performance with that of other control algorithms. The results showed that our proposed reward function performed better than the conventional single reward function in terms of the normalized root mean squared error and also showed a faster convergence trend. Furthermore, our control strategy converged within our desired performance index and could adapt to several walking speeds. Our proposed control structure has also an overall better performance compared to user-adaptive control, while some of its walking speeds performed better than the neural network predictive control from existing studies.

ACS Style

Yonatan Hutabarat; Kittipong Ekkachai; Mitsuhiro Hayashibe; Waree Kongprawechnon. Reinforcement Q-Learning Control With Reward Shaping Function for Swing Phase Control in a Semi-active Prosthetic Knee. Frontiers in Neurorobotics 2020, 14, 1 .

AMA Style

Yonatan Hutabarat, Kittipong Ekkachai, Mitsuhiro Hayashibe, Waree Kongprawechnon. Reinforcement Q-Learning Control With Reward Shaping Function for Swing Phase Control in a Semi-active Prosthetic Knee. Frontiers in Neurorobotics. 2020; 14 ():1.

Chicago/Turabian Style

Yonatan Hutabarat; Kittipong Ekkachai; Mitsuhiro Hayashibe; Waree Kongprawechnon. 2020. "Reinforcement Q-Learning Control With Reward Shaping Function for Swing Phase Control in a Semi-active Prosthetic Knee." Frontiers in Neurorobotics 14, no. : 1.

Journal article
Published: 27 January 2016 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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In recent years, intelligent prosthetic knees have been developed that enable amputees to walk as normally as possible when compared to healthy subjects. Although semi-active prosthetic knees utilizing magnetorheological (MR) dampers offer several advantages, they lack the ability to generate active force that is required during some states of a normal gait cycle. This prevents semi-active knees from achieving the same level of performance as active devices. In this work, a new control algorithm for a semi-active prosthetic knee during the swing phase is proposed to reduce this gap. The controller uses neural network predictive control and particle swarm optimization to calculate suitable command signals. Simulation results using a double pendulum model show that the generated knee trajectory of the proposed controller is more similar to the normal gait than previous open-loop controllers at various ambulation speeds. Moreover, the investigation shows that the algorithm can be calculated in real time by an embedded system, allowing for easy implementation on real prosthetic knees.

ACS Style

Kittipong Ekkachai; Itthisek Nilkhamhang. Swing Phase Control of Semi-Active Prosthetic Knee Using Neural Network Predictive Control With Particle Swarm Optimization. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2016, 24, 1169 -1178.

AMA Style

Kittipong Ekkachai, Itthisek Nilkhamhang. Swing Phase Control of Semi-Active Prosthetic Knee Using Neural Network Predictive Control With Particle Swarm Optimization. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2016; 24 (11):1169-1178.

Chicago/Turabian Style

Kittipong Ekkachai; Itthisek Nilkhamhang. 2016. "Swing Phase Control of Semi-Active Prosthetic Knee Using Neural Network Predictive Control With Particle Swarm Optimization." IEEE Transactions on Neural Systems and Rehabilitation Engineering 24, no. 11: 1169-1178.

Journal article
Published: 17 October 2013 in Smart Materials and Structures
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ACS Style

Kittipong Ekkachai; Kanokvate Tungpimolrut; Itthisek Nilkhamhang. Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network. Smart Materials and Structures 2013, 22, 1 .

AMA Style

Kittipong Ekkachai, Kanokvate Tungpimolrut, Itthisek Nilkhamhang. Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network. Smart Materials and Structures. 2013; 22 (11):1.

Chicago/Turabian Style

Kittipong Ekkachai; Kanokvate Tungpimolrut; Itthisek Nilkhamhang. 2013. "Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network." Smart Materials and Structures 22, no. 11: 1.

Journal article
Published: 01 January 2012 in ScienceAsia
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ACS Style

Kittipong Ekkachai; Kanokvate Tungpimolrut; Itthisek Nilkhamhang. A novel approach to model magneto-rheological dampers using EHM with a feed-forward neural network. ScienceAsia 2012, 38, 1 .

AMA Style

Kittipong Ekkachai, Kanokvate Tungpimolrut, Itthisek Nilkhamhang. A novel approach to model magneto-rheological dampers using EHM with a feed-forward neural network. ScienceAsia. 2012; 38 (4):1.

Chicago/Turabian Style

Kittipong Ekkachai; Kanokvate Tungpimolrut; Itthisek Nilkhamhang. 2012. "A novel approach to model magneto-rheological dampers using EHM with a feed-forward neural network." ScienceAsia 38, no. 4: 1.