Journal of Intelligent & Fuzzy Systems - Volume 40, issue 4

Authors: Liu, Hsiao-Man | Huang, Chung-Chi | Huang, Chung-Lin | Ke, Yen-Ting

Article Type: Research Article

Abstract: This study proposes a health assessment and predictive assistance system for intelligent health monitoring. Through machine learning, the tool features a customized set of quantitative measurements and web analysis systems for physical and mental fitness. The system replaces the manpower and time requirements of the past necessary to conduct interviews and keep paper records, allowing users to observe and analyze physical and mental fitness status through the webpage. To achieve this, ECG, EEG, and EMAS are used to follow physiological, psychological, and meridian energy states. ASP.NET software is used as a development tool for the system cloud page, which constructs, …documents, evaluates, and predicts functions for the smart health assistance system. The measurement data is entered and recorded in the cloud database. The data is used to construct an assessment and prediction of the user’s state of mind and body through machine learning methods, as well as the individual’s physical and mental fitness. Show more

Keywords: Intelligent assessment, intelligent prediction, somatic fitness, healthcare, machine learning

DOI: 10.3233/JIFS-189618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7957-7967, 2021

Authors: Huang, Chung-Lin | Huang, Chung-Chi

Article Type: Research Article

Abstract: Knowledge graphs are useful sources for various AI applications, however the basic paradigm to support pilot training is still unclear. In the paper, It is proposed to generate the customized knowledge graph of flight trainings using machine learning method for the flight training program. In order to provide the successful key to the further understanding of the learning problems between the students and the instructors. In this research, we collected data from an aeronautical academic in Taiwan that students were trained for Recreation Pilot License Program. We performed a test on 24 students at the first of each training course, …16 data of collected been used on building the module, 8 of them used to exam the module. There are 12 courses in the training program, and 30 hours total time were suggested by academic. The score which we applied on test were based on LCG method which is the sum of Maneuver and SRM Grades. For the indicators of course component in Learner Centered Grading, namely (a) CCS1: Operation & Effect of Controls; (b) CCS2: Straight & Level; (c) CCS3: Climbing & Descending; (d) CCS4: Turning; (e) CCS5: Stalling; (f) CCS6: Revision; (g) CCS7: Circuits; (h) CCS8: Cross-Wind Training; (i) CCS9: Circuit Emergency; (j) CCS10: Solo Circuit; (k) CCS11: Forced Landing; and (l) CCS12: Precautionary & Searching Landing. Through the method of Knowledge Graph, we deduct and predict the number of hours that need to be added for each student’s learning. Using the dynamic knowledge graph to display the key issues of the course learning continuously, and make follow-up decisions for the students, instructors and airliners. Show more

Keywords: Customized knowledge graph, FAA-industry training standards, machine learning

DOI: 10.3233/JIFS-189619

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7969-7979, 2021

Authors: Wen, Bor-Jiunn | Kao, Chia-Hung | Yeh, Che-Chih

Article Type: Research Article

Abstract: Labor force is gradually becoming insufficient owing to the aging population. The quality and safety of workforces are increasingly important, and thus, a set of intelligent wearable devices that assist the transport of loads by laborers, provide auxiliary standing support, and prevent falls were designed in this study. By applying an auxiliary force to the knee joint externally, an intelligent wearable device saves labor and reduces the burden on this joint, thereby protecting it. This study utilizes a Bayesian backpropagation algorithm for intelligent control. The intelligent wearable device provides the most suitable velocity and torsion depending on the initial driving …torsion of the user by a Bayesian backpropagation algorithm based on the current angle position, velocity, and torsion load of the device motor, thereby achieving an intelligent control effect of auxiliary standing support. A triaxial accelerometer is utilized to sense a fall and prevent it by a so-called fuzzy-Bayesian backpropagation control (FBC). Eventually, this study successfully designed and manufactured an intelligent wearable device by the FBC method. For a single motor control, two knee auxiliary devices can generate a torsion of 18.6 Nm. For dual motor control, two knee auxiliary devices can generate a torsion of 43.2 Nm. Thus, the laborers can not only perform their work efficiently and safely but also reduce costs and raise the working market competitiveness. Show more

Keywords: Intelligent wearable device, auxiliary stand, falling prevention, fuzzy-bayesian backpropagation control

DOI: 10.3233/JIFS-189620

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7981-7991, 2021

Authors: Pan, Nan | Shen, Xin | Guo, Xiaojue | Cao, Min | Pan, Dilin

Article Type: Research Article

Abstract: In recent years, electricity stealing has been repeatedly prohibited, and as the methods of stealing electricity have become more intelligent and concealed, it is growing increasingly difficult to extract high-dimensional data features of power consumption. In order to solve this problem, a correlation model of power-consumption data based on convolutional neural networks (CNN) is established. First, the original user signal is preprocessed to remove the noise. The user signal with a fixed signal length is then intercepted and the parallel class labelled. The segmented user signals and corresponding labels are input into the convolutional neural network for training, and the …trained convolutional neural network is then used to detect and classify the test user signals. Finally, the actual steal leak dataset is used to verify the effectiveness of this algorithm, which proves that the algorithm can effectively carry out anti–-electricity stealing by warning of abnormal power consumption behavior. There are lots of line traces on the surface of the broken ends which left in the cable cutting case crime scene along the high-speed railway in China. The line traces usually present nonlinear morphological features and has strong randomness. It is not very effective when using existing image-processing and three-dimensional scanning methods to do the trace comparison, therefore, a fast algorithm based on wavelet domain feature aiming at the nonlinear line traces is put forward to make fast trace analysis and infer the criminal tools. The proposed algorithm first applies wavelet decomposition to the 1-D signals which picked up by single point laser displacement sensor to partially reduce noises. After that, the dynamic time warping is employed to do trace feature similarity matching. Finally, using linear regression machine learning algorithm based on gradient descent method to do constant iteration. The experiment results of cutting line traces sample data comparison demonstrate the accuracy and reliability of the proposed algorithm. Show more

Keywords: Anti–electricity stealing, high-dimensional data features, convolutional neural network, early warning

DOI: 10.3233/JIFS-189621

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7993-7999, 2021

Authors: Jeong, Sang-Ki | Ji, Dea-Hyeong | Oh, Ji-Youn | Seo, Jung-Min | Choi, Hyeung-Sik

Article Type: Research Article

Abstract: In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance …by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12 ]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9 ]. Show more

Keywords: USV (Unmanned surface vehicles), RNN (Recurrent neural network), LSTM (Long short-term memory models), ANN-PID (Artificial neural networks-PID)

DOI: 10.3233/JIFS-189622

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8001-8011, 2021

Authors: Choi, Hey-Min | Kim, Min-Kyu | Yang, Hyun

Article Type: Research Article

Abstract: Recently, abnormally high water temperature (AHWT) phenomena are occurring more often due to the global warming and its impact. These phenomena have damaged extensively to the maritime economy around the southern coast of Korea and caused an illness by exacerbating the propagation of Vibrio pathogens. To mitigate damages by AHWT phenomena, it is necessary to respond as quickly as possible or predict them in advance. In this study, therefore, we proposed a deep learning-based methodology to predict the occurrences of AHWT phenomena using the long short-term memory (LSTM) model. First, a LSTM model was trained using the satellite-derived water temperature …data over the past ten years. Then, the water temperatures after a few days were estimated using the trained LSTM model. In a performance evaluation, when estimating water temperatures after one-day, the model achieved results of 1.865 and 0.412 in terms of mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively. Second, we developed a decision algorithm based on the Markov state transition in order to predict the AHWT occurrence probability. As a result, we obtained 0.88 of F1 score for predicting AHWT phenomena after 1 day in case of the southern coast of Korea. Show more

Keywords: Long short-term memory, deep learning, satellite data, abnormally high water temperature

DOI: 10.3233/JIFS-189623

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8013-8020, 2021

Authors: Phawinee, Suphawimon | Cai, Jing-Fang | Guo, Zhe-Yu | Zheng, Hao-Ze | Chen, Guan-Chen

Article Type: Research Article

Abstract: Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then …the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app. Show more

Keywords: Face recognition, intelligent lock, ResNet, deep learning

DOI: 10.3233/JIFS-189624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8021-8031, 2021

Authors: Wen, Yafeng

Article Type: Research Article

Abstract: With the promotion of BIM Technology, prefabricated building is developed rapidly in China. However, BIM technology has been only partially applied to prefabricated building, and there is still a gap between prefabricated building and intelligent construction. This paper focus on BIM 5D, together with relevant information technologies, all of which will be highly integrated and applied to prefabricated building, with the mission to get related information and enable the rapid flow of information, as well as bringing human perception, memory, knowledge and wisdom into prefabricated building, driving the development of prefabricated buildings to intelligence and leanness. Intelligent construction is …an innovated construction model based on the combination of latest information technology and engineering construction. Thus, it is particularly important to train personnel with corresponding knowledge structure, knowledge system and professional ability for intelligent construction. This paper also discusses about how to train personnel on prefabricated building and intelligent construction. Show more

Keywords: BIM5D, prefabricated building, intelligent construction, personnel training

DOI: 10.3233/JIFS-189625

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8033-8041, 2021

Authors: Hsieh, Wen-Hsiang | Chen, Yi-Syun | Wu, Shang-Teh

Article Type: Research Article

Abstract: Iterative Learning Control is a branch of intelligent control which combines artificial intelligence and control theory. This objective of this study aims at reducing the cyclic error of an inverse ball screw transmission system by using iterative learning control approach. Firstly, kinematic and dynamic analyses are conducted by using the vectorial loop closure and Lagrange equations, respectively. Then, system identification is performed followed by controller design. Moreover, controller parameters are optimized to minimize the error. Finally, the feasibility and the effectiveness of the proposed approach are verified by computer simulation and prototype experiment. The experimental results showed that the reducing …percentage of the square error sum of the output speed is 90.64% by using PID control only. If ILC is applied additionally, the error is further reduced to 94.21%. Therefore, the proposed approach is not only feasible and but also effective. Show more

Keywords: Ball screw, ILC controller, PID controller, Oldham coupling

DOI: 10.3233/JIFS-189627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8043-8052, 2021

Authors: Lai, Yi-Horng

Article Type: Research Article

Abstract: OBJECTIVES: Efavirenz therapy plays an important role in controlling the progression of HIV/AIDS. However, efavirenz often causes short-term side effects for the central nervous system, and it remained controversial as to whether efavirenz leads to depression or even suicidal attempt when applied for a longer period of time. The purpose of this study is to determine the association between the use of efavirenz and depressive disorders. METHODS: This study explored the use of efavirenz on HIV-infected patients using National Health Insurance Research Database (NHIRD) in Taiwan by Bayesian survival analysis and investigated whether the use of efavirenz has …the risk of depressive disorders. To reduce the dependence of statistical modeling assumptions, this study applied propensity score matching to research data. RESULTS: Based on the result of this study, it can be found that the use of efavirenz (HR = 1.009, 95% CI=–0.505 0.554), gender (HR = 0.324, 95% CI = –2.544 0.381) were not significantly associated with the occurrence of depressive disorders, whereas age of HIV diagnosis (HR = 1.021, 95% CI = 0.011 0.055) was significantly associated with the occurrence of depressive disorders. This study concludes that the use of efavirenz does not in-crease the risk of depressive disorders among HIV-treated patients. CONCLUSIONS: For the care of HIV-infected patients (especially the older ones), the psychological harm from society, such as lack of social support, social stigma or unemployment is higher than the harm of medicine. Show more

Keywords: Human immunodeficiency virus (HIV), active antiretroviral therapy, depressive disorder, propensity score matching, Bayesian cox regression

DOI: 10.3233/JIFS-189628

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8053-8062, 2021