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Ping-Huan Kuo
Advanced Institute of Manufacturing with High-Tech Innovations (AIM-HI), National Chung Cheng University, Chiayi 62102, Taiwan

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Journal article
Published: 29 July 2021 in Sensors
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In aspect of the natural language processing field, previous studies have generally analyzed sound signals and provided related responses. However, in various conversation scenarios, image information is still vital. Without the image information, misunderstanding may occur, and lead to wrong responses. In order to address this problem, this study proposes a recurrent neural network (RNNs) based multi-sensor context-aware chatbot technology. The proposed chatbot model incorporates image information with sound signals and gives appropriate responses to the user. In order to improve the performance of the proposed model, the long short-term memory (LSTM) structure is replaced by gated recurrent unit (GRU). Moreover, a VGG16 model is also chosen for a feature extractor for the image information. The experimental results demonstrate that the integrative technology of sound and image information, which are obtained by the image sensor and sound sensor in a companion robot, is helpful for the chatbot model proposed in this study. The feasibility of the proposed technology was also confirmed in the experiment.

ACS Style

Ping-Huan Kuo; Ssu-Ting Lin; Jun Hu; Chiou-Jye Huang. Multi-Sensor Context-Aware Based Chatbot Model: An Application of Humanoid Companion Robot. Sensors 2021, 21, 5132 .

AMA Style

Ping-Huan Kuo, Ssu-Ting Lin, Jun Hu, Chiou-Jye Huang. Multi-Sensor Context-Aware Based Chatbot Model: An Application of Humanoid Companion Robot. Sensors. 2021; 21 (15):5132.

Chicago/Turabian Style

Ping-Huan Kuo; Ssu-Ting Lin; Jun Hu; Chiou-Jye Huang. 2021. "Multi-Sensor Context-Aware Based Chatbot Model: An Application of Humanoid Companion Robot." Sensors 21, no. 15: 5132.

Journal article
Published: 04 February 2021 in Sustainability
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Electricity load forecasting is one of the hot concerns of the current electricity market, and many forecasting models are proposed to satisfy the market participants’ needs. Most of the models have the shortcomings of large computation or low precision. To address this problem, a novel deep learning and data processing ensemble model called SELNet is proposed. We performed an experiment with this model; the experiment consisted of two parts: data processing and load forecasting. In the data processing part, the autocorrelation function (ACF) was used to analyze the raw data on the electricity load and determine the data to be input into the model. The variational mode decomposition (VMD) algorithm was used to decompose the electricity load raw-data into a set of relatively stable modes named intrinsic mode functions (IMFs). According to the time distribution and time lag determined using the ACF, the input of the model was reshaped into a 24 × 7 × 8 matrix M, where 24, 7, and 8 represent 24 h, 7 days, and 8 IMFs, respectively. In the load forecasting part, a two-dimensional convolutional neural network (2D-CNN) was used to extract features from the matrix M. The improved reshaped layer was used to reshape the extracted features according to the time order. A temporal convolutional network was then employed to learn the reshaped time-series features and combined with the fully connected layer to complete the prediction. Finally, the performance of the model was verified in the Eastern Electricity Market of Texas. To demonstrate the effectiveness of the proposed model data processing and load forecasting, we compared it with the gated recurrent unit (GRU), TCN, VMD-TCN, and VMD-CNN models. The TCN exhibited better performance than the GRU in load forecasting. The mean absolute percentage error (MAPE) of the TCN, which was over 5%, was less than that of the GRU. Following the addition of VMD to the TCN, the basic performance of the model was 2–3%. A comparison between the SELNet model and the VMD-TCN model indicated that the application of a 2D-CNN improves the forecast performance, with only a few samples having an MAPE of over 4%. The model’s prediction effect in each season is discussed, and it was found that the proposed model can achieve high-precision prediction in each season.

ACS Style

Yamin Shen; Yuxuan Ma; Simin Deng; Chiou-Jye Huang; Ping-Huan Kuo. An Ensemble Model Based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting. Sustainability 2021, 13, 1694 .

AMA Style

Yamin Shen, Yuxuan Ma, Simin Deng, Chiou-Jye Huang, Ping-Huan Kuo. An Ensemble Model Based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting. Sustainability. 2021; 13 (4):1694.

Chicago/Turabian Style

Yamin Shen; Yuxuan Ma; Simin Deng; Chiou-Jye Huang; Ping-Huan Kuo. 2021. "An Ensemble Model Based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting." Sustainability 13, no. 4: 1694.

Journal article
Published: 10 November 2020 in IEEE Access
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This study proposed a new processing method to predict breast cancer on the basis of nine individual attributes, including age, body mass index, glucose, insulin, and a homeostasis model assessment. First, principal component analysis (PCA) was used to identify valuable parts of the data and further reduce the dimensions of the data. The cumulative proportion of the top five major components was 99.89%. The multilayer perceptron network (MLP) method was then used to extract characteristics included in the data, and the structure of the network was designed for the exploration of how data developed as the dimensions increased or decreased. As such, the model was established to first explore (high dimensional) and then develop (low dimensional) data. After training and learning, the models could segregate the representative attributes and numbers, and the characteristic data were then used as classifiers through transfer learning techniques using support vector machines. To verify the proposed method, the experiment performed $k$ -fold cross-validation 50 times on average. Experimental results verified the proposed method with 10-fold cross-validation using the dataset of Manuel Gomes from the University Hospital Centre of Coimbra, and an accuracy of 86.97% was achieved. The results indicate that the proposed series of processes and methods can effectively and powerfully examine the incidence of breast cancer. Furthermore, the data processed using only the PCA method as well as the characteristics extracted through the PCA method then combined with MLP after learning were analyzed. The differences displayed for the visual technique characteristics of the t-distributed stochastic neighbor embedding were compared.

ACS Style

Huan-Jung Chiu; Tzuu-Hseng S. Li; Ping-Huan Kuo. Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine. IEEE Access 2020, 8, 204309 -204324.

AMA Style

Huan-Jung Chiu, Tzuu-Hseng S. Li, Ping-Huan Kuo. Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine. IEEE Access. 2020; 8 ():204309-204324.

Chicago/Turabian Style

Huan-Jung Chiu; Tzuu-Hseng S. Li; Ping-Huan Kuo. 2020. "Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine." IEEE Access 8, no. : 204309-204324.

Journal article
Published: 22 October 2020 in IEEE Transactions on Fuzzy Systems
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In this study, the adaptive-network-based fuzzy inference system (ANFIS) is combined with the double deep Q-network (DDQN) to realize a fuzzy DDQN (FDDQN) such that a humanoid robot can generate a linear inverted pendulum model-based gait pattern in real time. The FDDQN not only allows the humanoid robot to correct the gait pattern instantly but also improves its stability. The proposed scheme is designed and implemented in a toddler-sized humanoid robot called Louis. First, four pressure sensors are installed on the bottom of the sole and one inertial measurement unit is set up on the trunk of the robot. A wireless communication chip is employed to transfer the data to a computer to determine the required parameters for the robot. Next, a control system based on the Linux operating system is developed. The values of the center of pressure and acceleration obtained with the ANFIS are adopted to train the DDQN. The proposed neural network comprises four layers, and the model is cautiously selected to avoid overfitting. The proposed scheme is verified using a robot simulator and then real-time-tested on Louis. The experimental results indicate that the FDDQN can provide the robot timely feedback during walking as well as help it in adjusting the gait pattern independently. The balancing of the robot through effective dynamic feedback is similar to the balancing ability of an infant learning to walk.

ACS Style

Tzuu-Hseng S. Li; Ping-Huan Kuo; Lin-Han Chen; Chia-Ching Hung; Po-Chien Luan; Hao-Ping Hsu; Chien-Hsin Chang; Yi-Ting Hsieh; Wen-Hsun Lin. Fuzzy Double Deep Q-Network-Based Gait Pattern Controller for Humanoid Robots. IEEE Transactions on Fuzzy Systems 2020, PP, 1 -1.

AMA Style

Tzuu-Hseng S. Li, Ping-Huan Kuo, Lin-Han Chen, Chia-Ching Hung, Po-Chien Luan, Hao-Ping Hsu, Chien-Hsin Chang, Yi-Ting Hsieh, Wen-Hsun Lin. Fuzzy Double Deep Q-Network-Based Gait Pattern Controller for Humanoid Robots. IEEE Transactions on Fuzzy Systems. 2020; PP (99):1-1.

Chicago/Turabian Style

Tzuu-Hseng S. Li; Ping-Huan Kuo; Lin-Han Chen; Chia-Ching Hung; Po-Chien Luan; Hao-Ping Hsu; Chien-Hsin Chang; Yi-Ting Hsieh; Wen-Hsun Lin. 2020. "Fuzzy Double Deep Q-Network-Based Gait Pattern Controller for Humanoid Robots." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.

Journal article
Published: 09 September 2020 in IEEE Access
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Spherical porous air bearing (SPAB) systems have been extensively used in various mechanical engineering applications. SPABs are promising materials in high-rotational speed, high-precision, and highstiffness instruments. In SPAB systems, a rotor is supported by gas bearings, which provides higher rotational speed and lower heat generation environment than oil bearings do. Furthermore, SPAB does not cause deformation. Although, the supporting force of gas bearings is less, their stability is better than that of oil films. However, because the pressure distribution in the gas films is nonlinear, they are prone to failure at specific critical speeds, rotor imbalances, or inappropriate operations, which results in nonperiodic or chaotic motion and causes structural fatigue to the system. To understand and control the operating conditions of the SPAB systems during the nonperiodic motion, first, the governing equations of the SPAB system were solved to obtain the dynamic behavior of the rotor center. Then, the performance of the SPAB system were examined under different operating conditions by generating the maximum Lyapunov exponents (MLEs). However, the calculation process of MLE is extremely time consuming and complex. To solve this problem efficiently, a high-precision machine learning (ML)–based MLE prediction model was proposed in this study. The results show that the training process can be finished within few minutes, and the prediction process is able to be completed within few seconds. Meanwhile, the results demonstrate the merit of using the machine learning method for solving the MLE prediction problem and shorten the calculation time significantly. The proposed prediction model achieves excellent prediction outcome and it is more efficient and precise than traditional iteration scheme for the calculation of MLE. The feasibility of the proposed model is validated and the results also are the major contribution of this study.

ACS Style

Ping-Huan Kuo; Rong-Mao Lee; Cheng-Chi Wang. A High-Precision Random Forest-Based Maximum Lyapunov Exponent Prediction Model for Spherical Porous Gas Bearing Systems. IEEE Access 2020, 8, 168079 -168086.

AMA Style

Ping-Huan Kuo, Rong-Mao Lee, Cheng-Chi Wang. A High-Precision Random Forest-Based Maximum Lyapunov Exponent Prediction Model for Spherical Porous Gas Bearing Systems. IEEE Access. 2020; 8 (99):168079-168086.

Chicago/Turabian Style

Ping-Huan Kuo; Rong-Mao Lee; Cheng-Chi Wang. 2020. "A High-Precision Random Forest-Based Maximum Lyapunov Exponent Prediction Model for Spherical Porous Gas Bearing Systems." IEEE Access 8, no. 99: 168079-168086.

Journal article
Published: 17 August 2020 in IEEE Sensors Journal
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This study proposes a real-time sequential sensor fusion based gait pattern controller. During the walking cycle, the sensor data is analyzed and obtained by several inertial measurement units (IMUs) and pressure sensors on the robot. Besides, in order to model the correction values of the walking parameters, this approach applies long short-term memory (LSTM) to build a feedback system for a bipedal humanoid robot. For training the network, four sequential features including two-direction Center of Pressure (CoP) and two-direction acceleration are acquired by using pressure sensors and IMUs on the robot. While these features are mapped to the network inputs, the network output is mapped by the optimal solutions generated using particle swarm optimization (PSO). The proposed LSTM feedback network comprises four layers. The effect of the proposed methodology is first tested in a robot simulation environment and then tested on a real bipedal humanoid robot. The experimental results demonstrate that this methodology can make the humanoid robot perform better than when using a fixed gait. It also provides self-adjustment capability to the robot to enable it to avoid situations like falling.

ACS Style

Tzuu-Hseng S. Li; Ping-Huan Kuo; Chuan-Han Cheng; Chia-Ching Hung; Po-Chien Luan; Chien-Hsin Chang. Sequential Sensor Fusion-Based Real-Time LSTM Gait Pattern Controller for Biped Robot. IEEE Sensors Journal 2020, 21, 2241 -2255.

AMA Style

Tzuu-Hseng S. Li, Ping-Huan Kuo, Chuan-Han Cheng, Chia-Ching Hung, Po-Chien Luan, Chien-Hsin Chang. Sequential Sensor Fusion-Based Real-Time LSTM Gait Pattern Controller for Biped Robot. IEEE Sensors Journal. 2020; 21 (2):2241-2255.

Chicago/Turabian Style

Tzuu-Hseng S. Li; Ping-Huan Kuo; Chuan-Han Cheng; Chia-Ching Hung; Po-Chien Luan; Chien-Hsin Chang. 2020. "Sequential Sensor Fusion-Based Real-Time LSTM Gait Pattern Controller for Biped Robot." IEEE Sensors Journal 21, no. 2: 2241-2255.

Research article
Published: 20 May 2020 in International Journal of Distributed Sensor Networks
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Linear predictive coding is an extremely effective voice generation method that operates through simple process. However, linear predictive coding–generated voices have limited variations and exhibit excessive noise. To resolve these problems, this article proposes an artificial intelligence model that combines a denoise autoencoder with generative adversarial networks. This model generates voices with similar semantics through the random input from the latent space of generator. The experimental results indicate that voices generated exclusively by generative adversarial networks exhibit excessive noise. To solve this problem, a denoise autoencoder was connected to the generator for denoising. The experimental results prove the feasibility of the proposed voice generation method. In the future, this method can be applied in robots and voice generation applications to increase the humanistic language expression ability of robots and enable robots to demonstrate more humanistic and natural speaking performance.

ACS Style

Ping-Huan Kuo; Ssu-Ting Lin; Jun Hu. DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network. International Journal of Distributed Sensor Networks 2020, 16, 1 .

AMA Style

Ping-Huan Kuo, Ssu-Ting Lin, Jun Hu. DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network. International Journal of Distributed Sensor Networks. 2020; 16 (5):1.

Chicago/Turabian Style

Ping-Huan Kuo; Ssu-Ting Lin; Jun Hu. 2020. "DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network." International Journal of Distributed Sensor Networks 16, no. 5: 1.

Other
Published: 05 May 2020
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The coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which are commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those of the other models. This indicated that the proposed framework can accurately predict the accumulated number of confirmed cases in the three countries and serve as a crucial reference for devising public health strategies.

ACS Style

Chiou-Jye Huang; Yamin Shen; Ping-Huan Kuo; Yung-Hsiang Chen. Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019. 2020, 1 .

AMA Style

Chiou-Jye Huang, Yamin Shen, Ping-Huan Kuo, Yung-Hsiang Chen. Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019. . 2020; ():1.

Chicago/Turabian Style

Chiou-Jye Huang; Yamin Shen; Ping-Huan Kuo; Yung-Hsiang Chen. 2020. "Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019." , no. : 1.

Other
Published: 27 March 2020
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COVID-19 is spreading all across the globe. Up until March 23, 2020, the confirmed cases in 173 countries and regions of the globe had surpassed 346,000, and more than 14,700 deaths had resulted. The confirmed cases outside of China had also reached over 81,000, with over 3,200 deaths. In this study, a Convolutional Neural Network (CNN) was proposed to analyze and predict the number of confirmed cases. Several cities with the most confirmed cases in China were the focus of this study, and a COVID-19 forecasting model, based on the CNN deep neural network method, was proposed. To compare the overall efficacies of different algorithms, the indicators of mean absolute error and root mean square error were applied in the experiment of this study. The experiment results indicated that compared with other deep learning methods, the CNN model proposed in this study has the greatest prediction efficacy. The feasibility and practicality of the model in predicting the cumulative number of COVID-19 confirmed cases were also verified in this study.

ACS Style

Chiou-Jye Huang; Yung-Hsiang Chen; Yuxuan Ma; Ping-Huan Kuo. Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China. 2020, 1 .

AMA Style

Chiou-Jye Huang, Yung-Hsiang Chen, Yuxuan Ma, Ping-Huan Kuo. Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China. . 2020; ():1.

Chicago/Turabian Style

Chiou-Jye Huang; Yung-Hsiang Chen; Yuxuan Ma; Ping-Huan Kuo. 2020. "Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China." , no. : 1.

Journal article
Published: 13 November 2019 in IEEE Access
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A cognition learning algorithm based on a deep belief network and inertia weight Particle Swarm Optimization (PSO) is presented and examined in a humanoid robot. The psychology concepts were adopted from Thinking, Fast and Slow by Daniel Kahneman. The human brain comprises two systems, System 1 and System 2. Based on their characteristics, System 1 and System 2 handle different tasks during cerebration. In this study, Deep Belief Network (DBN) is trained to construct the function of System 1 for the rapid reaction. On the other hand, PSO is applied to build System 2 for the slow and complicated brain behavior. Through the cooperation of System 1 and System 2, the proposed cognition learning algorithm can apply the psychology theories to allow the humanoid robot for learning the suitable pitching postures autonomously. In the experiments conducted in this study, the robot was trained for only five selected points and was then asked to throw precisely to nine points. The proposed algorithm provided 100% accuracy in the robot pitching game. The feasibility of the proposed algorithm was thus verified.

ACS Style

Tzuu-Hseng S. Li; Ping-Huan Kuo; Chien-Yu Chang; Hao-Ping Hsu; Yuan-Chih Chen. Deep Belief Network–Based Learning Algorithm for Humanoid Robot in a Pitching Game. IEEE Access 2019, 7, 165659 -165670.

AMA Style

Tzuu-Hseng S. Li, Ping-Huan Kuo, Chien-Yu Chang, Hao-Ping Hsu, Yuan-Chih Chen. Deep Belief Network–Based Learning Algorithm for Humanoid Robot in a Pitching Game. IEEE Access. 2019; 7 (99):165659-165670.

Chicago/Turabian Style

Tzuu-Hseng S. Li; Ping-Huan Kuo; Chien-Yu Chang; Hao-Ping Hsu; Yuan-Chih Chen. 2019. "Deep Belief Network–Based Learning Algorithm for Humanoid Robot in a Pitching Game." IEEE Access 7, no. 99: 165659-165670.

Journal article
Published: 02 September 2019 in IEEE Access
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This paper proposes an intelligent control strategy for enabling a robotic arm to grasp and place water-filled bottles without spilling any of the water. First, the system architecture of a five-degree-offreedom robotic arm and its mechanical design are introduced. Second, both the forward and inverse kinematics of the robotic arm are derived. The study conducted an experiment in which the designed and implemented robotic arm could grasp a bottle of water and move it to another place. However, if the acceleration or the orientation of the robotic arm were inappropriate, the water in the bottle may be spilled during the movement. Therefore, the proposed strategy applies an inertial measurement unit for obtaining relevant information. According to the obtained information, the velocity curves of each joint could be optimized by adaptive inertia weight and acceleration coefficients particle swarm optimization. Finally, the experimental results demonstrated the feasibility and effectiveness of the proposed method.

ACS Style

Tzuu-Hseng S. Li; Ping-Huan Kuo; Ya-Fang Ho; Guan-Hong Liou. Intelligent Control Strategy for Robotic Arm by Using Adaptive Inertia Weight and Acceleration Coefficients Particle Swarm Optimization. IEEE Access 2019, 7, 126929 -126940.

AMA Style

Tzuu-Hseng S. Li, Ping-Huan Kuo, Ya-Fang Ho, Guan-Hong Liou. Intelligent Control Strategy for Robotic Arm by Using Adaptive Inertia Weight and Acceleration Coefficients Particle Swarm Optimization. IEEE Access. 2019; 7 (99):126929-126940.

Chicago/Turabian Style

Tzuu-Hseng S. Li; Ping-Huan Kuo; Ya-Fang Ho; Guan-Hong Liou. 2019. "Intelligent Control Strategy for Robotic Arm by Using Adaptive Inertia Weight and Acceleration Coefficients Particle Swarm Optimization." IEEE Access 7, no. 99: 126929-126940.

Journal article
Published: 11 July 2019 in IEEE Access
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Robots must be able to recognize human emotions to improve the human-robot interaction (HRI). This study proposes an emotion recognition system for a humanoid robot. The robot is equipped with a camera to capture users' facial images, and it uses this system to recognize users' emotions and responds appropriately. The emotion recognition system, based on a deep neural network, learns six basic emotions: happiness, anger, disgust, fear, sadness, and surprise. First, a convolutional neural network (CNN) is used to extract visual features by learning on a large number of static images. Second, a long short-term memory (LSTM) recurrent neural network is used to determine the relationship between the transformation of facial expressions in image sequences and the six basic emotions. Third, CNN and LSTM are combined to exploit their advantages in the proposed model. Finally, the performance of the emotion recognition system is improved by using transfer learning, that is, by transferring knowledge of related but different problems. The performance of the proposed system is verified through leave-one-out cross-validation and compared with that of other models. The system is applied to a humanoid robot to demonstrate its practicability for improving the HRI.

ACS Style

Tzuu-Hseng S. Li; Ping-Huan Kuo; Ting-Nan Tsai; Po-Chien Luan. CNN and LSTM Based Facial Expression Analysis Model for a Humanoid Robot. IEEE Access 2019, 7, 93998 -94011.

AMA Style

Tzuu-Hseng S. Li, Ping-Huan Kuo, Ting-Nan Tsai, Po-Chien Luan. CNN and LSTM Based Facial Expression Analysis Model for a Humanoid Robot. IEEE Access. 2019; 7 ():93998-94011.

Chicago/Turabian Style

Tzuu-Hseng S. Li; Ping-Huan Kuo; Ting-Nan Tsai; Po-Chien Luan. 2019. "CNN and LSTM Based Facial Expression Analysis Model for a Humanoid Robot." IEEE Access 7, no. : 93998-94011.

Journal article
Published: 06 June 2019 in IEEE Access
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With the fast expansion of renewable energy system installed capacity in recent years, the availability, stability and quality of smart grids has become increasingly important. Renewable energy output forecasting applications has also been developing rapidly in recent years, such techniques have particularly been applied in the fields of wind and solar photovoltaic (PV). In the case of solar PV output forecasting, many applications have been performed with machine learning and hybrid techniques. In this paper, we propose a high-precision deep neural network model named PVPNet to forecast PV system output power. The methodology behind the proposed model is based on deep neural networks, the model is able to generate a 24-hour probabilistic and deterministic forecasting of PV power output, based on meteorological information such as temperature, solar radiation and historical PV system output data. The forecasting accuracy of PVPNet is determined by the mean absolute error (MAE) and root mean square error (RMSE) values. Results from the experiments show that the MAE and RMSE of the proposed algorithm are 109.4845 and 163.1513, respectively. The results prove that the prediction accuracy of PVPNet outperforms other benchmark models, the algorithm also effectively predicts complex time series with a high degree of volatility and irregularity.

ACS Style

Chiou-Jye Huang; Ping-Huan Kuo. Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting. IEEE Access 2019, 7, 74822 -74834.

AMA Style

Chiou-Jye Huang, Ping-Huan Kuo. Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting. IEEE Access. 2019; 7 (99):74822-74834.

Chicago/Turabian Style

Chiou-Jye Huang; Ping-Huan Kuo. 2019. "Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting." IEEE Access 7, no. 99: 74822-74834.

Technical note
Published: 13 February 2019 in Energies
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In many industries and medical power system applications, dual power source design is often used to ensure that the equipment runs continuously, even when the main power supply is shut down. However, the voltage feedback between two independent power supplies and low loss output are critical issues for the system energy dissipation. Without using a dedicated chip, a new mutual blocking control technology is proposed in this paper to effectively solve the problem of voltage feedback existing in the conventional dual power system. In addition, without adding much hardware cost, the proposed dual power switch design can completely avoid voltage feedback and achieve a low voltage loss of about 30 mV when the load current is less than 0.5 A.

ACS Style

Hsin-Chuan Chen; Ping-Huan Kuo; Chiou-Jye Huang. A Mutual Blocking Technology Applied to Dual Power Source Switching Control. Energies 2019, 12, 576 .

AMA Style

Hsin-Chuan Chen, Ping-Huan Kuo, Chiou-Jye Huang. A Mutual Blocking Technology Applied to Dual Power Source Switching Control. Energies. 2019; 12 (4):576.

Chicago/Turabian Style

Hsin-Chuan Chen; Ping-Huan Kuo; Chiou-Jye Huang. 2019. "A Mutual Blocking Technology Applied to Dual Power Source Switching Control." Energies 12, no. 4: 576.

Journal article
Published: 01 January 2019 in Applied Mechanics and Materials
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With the development of the concept of Industry 4.0, research relating to robots is being paid more and more attention, among which the humanoid robot is a very important research topic. The humanoid robot is a robot with a bipedal mechanism. Due to the physical mechanism, humanoid robots can maneuver more easily in complex terrains, such as going up and down the stairs. However, humanoid robots often fall from imbalance. Whether or not the robot can stand up on its own after a fall is a key research issue. However, the often used method of hand tuning to allow robots to stand on its own is very inefficient. In order to solve the above problems, this paper proposes an automatic learning system based on Particle Swarm Optimization (PSO). This system allows the robot to learn how to achieve the motion of rebalancing after a fall. To allow the robot to have the capability of object recognition, this paper also applies the Convolutional Neural Network (CNN) to let the robot perform image recognition and successfully distinguish between 10 types of objects. The effectiveness and feasibility of the motion learning algorithm and the CNN based image classification for vision system proposed in this paper has been confirmed in the experimental results.

ACS Style

Ssu Ting Lin; Jun Hu; Chia Hung Shih; Chiou Jye Huang; Ping Huan Kuo. The Development of Supervised Motion Learning and Vision System for Humanoid Robot. Applied Mechanics and Materials 2019, 886, 188 -193.

AMA Style

Ssu Ting Lin, Jun Hu, Chia Hung Shih, Chiou Jye Huang, Ping Huan Kuo. The Development of Supervised Motion Learning and Vision System for Humanoid Robot. Applied Mechanics and Materials. 2019; 886 ():188-193.

Chicago/Turabian Style

Ssu Ting Lin; Jun Hu; Chia Hung Shih; Chiou Jye Huang; Ping Huan Kuo. 2019. "The Development of Supervised Motion Learning and Vision System for Humanoid Robot." Applied Mechanics and Materials 886, no. : 188-193.

Journal article
Published: 16 October 2018 in Energies
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To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

ACS Style

Chiou-Jye Huang; Ping-Huan Kuo. A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems. Energies 2018, 11, 2777 .

AMA Style

Chiou-Jye Huang, Ping-Huan Kuo. A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems. Energies. 2018; 11 (10):2777.

Chicago/Turabian Style

Chiou-Jye Huang; Ping-Huan Kuo. 2018. "A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems." Energies 11, no. 10: 2777.

Journal article
Published: 10 July 2018 in Sensors
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In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

ACS Style

Chiou-Jye Huang; Ping-Huan Kuo. A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities. Sensors 2018, 18, 2220 .

AMA Style

Chiou-Jye Huang, Ping-Huan Kuo. A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities. Sensors. 2018; 18 (7):2220.

Chicago/Turabian Style

Chiou-Jye Huang; Ping-Huan Kuo. 2018. "A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities." Sensors 18, no. 7: 2220.

Journal article
Published: 29 May 2018 in Energies
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The power generation potential of a solar photovoltaic (PV) power generation system is closely related to the on-site solar radiation, and sunshine conditions are an important reference index for evaluating the installation of a solar PV system. Meanwhile, the long-term operation and maintenance of a PV system needs solar radiation information as a reference for system performance evaluation. Obtaining solar radiation information through the installation of irradiation monitoring stations is often very costly, and the cost of sustaining the reliability of the monitoring system, Internet stability and subsequent operation and maintenance can often be alarming. Therefore, the establishment of a solar radiation estimation model can reduce the installation of monitoring stations and decrease the cost of obtaining solar radiation information. In this study, we use an inverse distance weighting algorithm to establish the solar radiation estimation model. The model was built by obtaining information from 20 solar radiation monitoring stations in central and southern Taiwan, and field verification was implemented at Yuan Chang Township town hall and the Tainan Liujia campus. Furthermore, a full comparison between Inverse Distance Weighting (IDW) and the Kriging method is also given in this paper. The estimation results demonstrate the performance of the IDW method. In the experiment, the performance of the IDW method is better than the Ordinary Kriging (OK) method. The Mean Absolute Percentage Error (MAPE) values of the solar radiation estimation model by IDW at the two field verifications were 4.30% and 3.71%, respectively.

ACS Style

Ping-Huan Kuo; Hsin-Chuan Chen; Chiou-Jye Huang. Solar Radiation Estimation Algorithm and Field Verification in Taiwan. Energies 2018, 11, 1374 .

AMA Style

Ping-Huan Kuo, Hsin-Chuan Chen, Chiou-Jye Huang. Solar Radiation Estimation Algorithm and Field Verification in Taiwan. Energies. 2018; 11 (6):1374.

Chicago/Turabian Style

Ping-Huan Kuo; Hsin-Chuan Chen; Chiou-Jye Huang. 2018. "Solar Radiation Estimation Algorithm and Field Verification in Taiwan." Energies 11, no. 6: 1374.

Journal article
Published: 21 April 2018 in Sustainability
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Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

ACS Style

Ping-Huan Kuo; Chiou-Jye Huang. An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks. Sustainability 2018, 10, 1280 .

AMA Style

Ping-Huan Kuo, Chiou-Jye Huang. An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks. Sustainability. 2018; 10 (4):1280.

Chicago/Turabian Style

Ping-Huan Kuo; Chiou-Jye Huang. 2018. "An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks." Sustainability 10, no. 4: 1280.

Journal article
Published: 02 April 2018 in Energies
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The photovoltaic (PV) systems generate green energy from the sunlight without any pollution or noise. The PV systems are simple, convenient to install, and seldom malfunction. Unfortunately, the energy generated by PV systems depends on climatic conditions, location, and system design. The solar radiation forecasting is important to the smooth operation of PV systems. However, solar radiation detected by a pyranometer sensor is strongly nonlinear and highly unstable. The PV energy generation makes a considerable contribution to the smart grids via a large number of relatively small PV systems. In this paper, a high-precision deep convolutional neural network model (SolarNet) is proposed to facilitate the solar radiation forecasting. The proposed model is verified by experiments. The experimental results demonstrate that SolarNet outperforms other benchmark models in forecasting accuracy as well as in predicting complex time series with a high degree of volatility and irregularity.

ACS Style

Ping-Huan Kuo; Chiou-Jye Huang. A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model. Energies 2018, 11, 819 .

AMA Style

Ping-Huan Kuo, Chiou-Jye Huang. A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model. Energies. 2018; 11 (4):819.

Chicago/Turabian Style

Ping-Huan Kuo; Chiou-Jye Huang. 2018. "A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model." Energies 11, no. 4: 819.