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Long-term depression and negative emotional cycles affect life quality and work productivity. However, depression is not easy to detect, with current methods mostly relying on the use of scales that make it impossible to quickly and directly measure the severity of depression. This research seeks to empirically identify brainwave stimulation feedback electrode points and brain regions related to potential depression. Using brainwave data collected by mood-induction procedures, the front and occipital lobes as found to have the greatest role in the operation of depressive emotions, especially the Fp1 and Fp2 positions and the O1 and O2 positions. The Fourier brainwave bands are mainly affected in α and θ band, while the wavelet brainwave bands have a significant impact on the minimum value of approximated signals. This research uses two signal processing methods, combined with deep neural network techniques (Multilayer perceptron, Deep neural network, Deep belief network, and Long Short-Term Memory) to develop 8 potential depression assessment models, with models constructed using deep neural networks providing the best and most stable performance. Therefore, this model can be developed as an auxiliary system for rapid and objective assessment of underlying depression, thereby assisting in the autonomous management of emotions, as well as the early detection and treatment of depression. In addition, in the low mood stage, individual abnormality is found, and appropriate relief methods are provided, potentially reducing the occurrence of depression.

Cyber-physical-social systems (CPSS) as an emerging computing paradigm are revolutionizing the relationship between humans, devices and physical environments and will be expected to have a fundamental impact and influence in our daily lives. In a typical CPSS scenario, plenty of nodes (or devices) often adopt wireless distributed contention protocols to transmit sensed environment states for decision making. Therefore, designing a high-efficient and collision-less contention protocol is of great importance for wireless networking. As is known, in the classic time-to-frequency (T2F) protocol, each node signals on one randomly chosen subcarrier for contention. Though reducing the conventional time-domain contention time, T2F leads to many contention collisions (i.e., multiple nodes choose the same subcarrier) when the number of nodes is large. Aiming at this challenge, in this paper, we propose M-T2F (where each node signals on multiple randomly chosen subcarriers for contention) and analyze its consensus achievement. Specifically, M-T2F supports more nodes for transmission but introduces less collision, benefiting from lots of multicarrier choice combinations. Since the subset relationship between subcarrier choices poses a challenge to reach a consensus among nodes, M-T2F enables all nodes to reach a consensus by excluding the subset relationship and reduces contention time by utilizing wireless broadcast characteristic

Robotic musicianship research aims to the configuration of robots can analyze, reason, and generate music autonomous. The goal of this research is to achieve the inspiring and meaningful musical interactions between humans and artificially creative robots. This research presents a new model of automatic music generation which is based on least squares and generative adversarial networks (GANs). This research specifies classical piano as the music source and uses the sensors as the context-awareness technology to sense and receive the input audio in human-robot interaction. Therefore, this research applies sequence generation adversarial network (SeqGAN) techniques that are better able to address discrete issues in generating samples of classical piano melodies and datasets. Modifying the SeqGAN approach, this research presented the Least Squares SeqGAN (LS-SeqGAN) method to create melody units on different chords and generates a set of music pieces as testing dataset. In this research, we implement the original method and use the least squares method to stabilize the training of GANs. The performance evaluation shows that proposed LS-SeqGAN method can fulfill the need both of music quality and creativity. It offers a robust infrastructure for the human-robotic interaction that can be used to promote the related robotic applications.

In Industrial Internet of Things (IIoT), a number of mutually untrusted devices with diverse computing resources and application requirements often need to work together to serve industrial production. When applying permissionless blockchain to process transactions among these devices, we are concerned with two types of fairness: mining fairness (i.e., miners should obtain commensurate rewards according to their respective invested computing power) and transaction-processing fairness (i.e., transaction packing and confirming is in a desired fair order). To address the two types of fairness simultaneously, this paper proposes GT-Chain, in which miners select transactions according to Geometric-distribution and propagate transactions with a Time-to-live limit for achieving transaction-processing fairness as well as balancing the processing efficiency and fairness. Besides, GT-Chain inherits properties of a famous blockchain called FruitChain for achieving mining fairness. We then conduct a probability analysis to quantify the efficiency of transaction processing and define a Euclidean-distance-based fair degree to measure the fairness of transaction processing. Extensive simulations verify that GT-Chain can well achieve our design objectives, and our theoretical model is accurate. This paper is helpful in better designing blockchain protocols for IIoT.

In this study, a high accident risk prediction model is developed to analyze traffic accident data, and identify priority intersections for improvement. A database of the traffic accidents was organized and analyzed, and an intersection accident risk prediction model based on different mechanical learning methods was created to estimate the possible high accident risk locations for traffic management departments to use in planning countermeasures to reduce accident risk. Using Bayes' theorem to identify environmental variables at intersections that affect accident risk levels, this study found that road width, speed limit and roadside markings are the significant risk factors for traffic accidents. Meanwhile, Naive Bayes, Decision tree C4.5, Bayesian Network, Multilayer perceptron (MLP), Deep Neural Networks (DNN), Deep Belief Network (DBN) and Convolutional Neural Network (CNN) were used to develop an accident risk prediction model. This model can also identify the key factors that affect the occurrence of high-risk intersections, and provide traffic management departments with a better basis for decision-making for intersection improvement. Using the same environmental characteristics as high-risk intersections for model inputs to estimate the degree of risk that may occur in the future, which can be used to prevent traffic accidents in the future. Moreover, it also can be used as a reference for future intersection design and environmental improvements.

The heart is one of the most important organs of the human body. It circulates blood throughout the human body and delivers oxygen and nutrients to all the organs for metabolism. Cardiac muscle contraction results in blood circulation, which maintains the body temperature at approximately 37 °C. If the cardiac function is abnormal, then the body temperature will be affected. The cardiac function degenerates as the human body ages, and the degeneration can occasionally result in cardiovascular diseases. When the Internet of Medical Things is integrated into heart disease screening systems to detect heart diseases, people can perform self-examinations to evaluate whether their hearts exhibit irregularities for early heart disease detection. STM32 is used in this study as the main Internet-of-Medical Things controller and is combined with the Internet of Things devices—a sphygmomanometer cuff, temperature sensor, and pulse sensor—for instrument control and data acquisition. This assembly is used to develop a valvular heart disease screening system, whose structure incorporates deep learning for the development of fitting models and analysis. An experiment is performed where blood flow is blocked temporarily and released to observe changes in the surface temperature of the fingertip skin, and the blood supply capability of the heart is assessed indirectly based on the temperature change curve. Eighteen subjects were recruited in the experiment, where one subject exhibited cardiac valve insufficiency and arrhythmia. In the experiment, temperature curve variation data are successfully obtained from the healthy subjects, whereas the temperature curve irregularities of the patient with cardiac valve insufficiency are identified. This subject’s temperature range throughout three test steps is smaller by within 0.52 °C compared with those of most of the other subjects. In addition, during blood blocking and release, the overall temperature curve decreases, whereas some curves escalate first before plummeting slowly. The data analysis results show that the temperature curve variation and values of Subject 2 are similar to those of Subject 10, suggesting the incidence of valvular heart disease. This valvular heart disease screening system can successfully analyze and assess the characteristic signal values of patients with valvular heart disease.

Topological data analysis is a new theoretical trend using topological techniques to mine data. This approach helps determine topological data structures. It focuses on investigating the global shape of data rather than on local information of high-dimensional data. The Mapper algorithm is considered as a sound representative approach in this area. It is used to cluster and identify concise and meaningful global topological data structures that are out of reach for many other clustering methods. In this article, we propose a new method called the Shape Fuzzy C -Means (SFCM) algorithm, which is constructed based on the Fuzzy C -Means algorithm with particular features of the Mapper algorithm. The SFCM algorithm can not only exhibit the same clustering ability as the Fuzzy C -Means but also reveal some relationships through visualizing the global shape of data supplied by the Mapper. We present a formal proof and include experiments to confirm our claims. The performance of the enhanced algorithm is demonstrated through a comparative analysis involving the original algorithm, Mapper, and the other fuzzy set based improved algorithm, F-Mapper, for synthetic and real-world data. The comparison is conducted with respect to output visualization in the topological sense and clustering stability.

This study applies statistical process control and machine learning techniques to diagnose wind turbine faults and predict maintenance needs by analyzing 2.8 million sensor data collected from 31 wind turbines from 2015 to 2017 in Taiwan. Unlike previous studies that only relied on historical wind turbine data, this study analyzed the sensor data with practitioners' insight by incorporating maintenance check list items into the data mining processes. We used Pareto analyses, scatter plots, and the cause and effect diagram to cluster and classify the failure types of wind turbines. In addition, control charts were used to establish a monitoring mechanism to track whether operation data are deviated from the controls (i.e., standard deviations) as a mean to detect wind turbine abnormalities. While statistical process control was applied to fault diagnosis, machine learning algorithms were used to predict maintenance needs of wind turbines. First, the density-based spatial clustering of applications with noise algorithm was used to classify abnormal-state wind turbine data from normal-state data. Then, random forest and decision tree algorithms were employed to construct the predictive models for wind turbine anomalies and tested with K-fold cross-validation. The results indicate a high level of accuracy: 92.68% for the decision tree model, and 91.98% for the random forest model. The study demonstrates that, by data mining and modeling, the failures of wind turbines can be detected, and the maintenance needs of parts can be predicted. Model results may provide technicians early warnings, improve equipment efficient, and decrease system downtime of wind turbine operation.

The development of social media has changed the way that travelers visit sightseeing spots. The social Internet of Things (IoT) allows products to automatically generate posts, share content and location information, and help build an online community of users based on their company’s products, so that marketing personnel can also get useful feedback and understand the user’s opinions. In tourism and hospitality industry, to enhance the revisit intention of passengers is an important issue for the purpose of increasing margin. In recent years, related researches had focused on the customers’ revisit behaviors and factors. However, few studies have investigated the related issues that travelers do not want to visit again. Failure to revisit may bring a great damage to the company’s revenue in the future. To avoid this situation, a text mining based approach will be proposed to identify non-revisit factors from online textual reviews in social media. Because it is impossible to determine whether a passenger has intention to revisit, this study proposed a text mining based approach which uses sentiment of text reviews to identify the passenger’s motivations (negative for revisit and non-negative for revisit). Then, feature selection methods, decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machines Recursive Feature Elimination (SVM-RFE) will be utilized to discover the important factors of non-revisit factor set. Back-propagation Neural Networks (BPN) and Support Vector Machines (SVM) will be employed to evaluate the effectiveness of selected feature sets. Finally, experimental results could be provided to travel service providers to improve service quality and effectively avoid non-revisit behaviors in the future.

Epilepsy is a common neurological disease that can cause seizures and loss of consciousness and can have a severe negative impact on long-term cognitive function. Reducing the severity of impact requires early diagnosis and treatment. Epilepsy is traditionally diagnosed using electroencephalography (EEG) performed by trained physicians or technicians but this process is time-consuming and prone to interference, which can negatively impact accuracy. This paper develops a model for epilepsy diagnosis using discrete wavelet transform to analyze sub-bands within the EEG parameter and select EEG characteristics for epilepsy detection. The minimize entropy principle approach is used to build fuzzy membership functions of the characteristics of each brain wave and are then used as the basis for the construction of an associative Petri net model. Using our APN model, the associative Petri net approach provides diagnosis accuracy rates of 93.8%, outperforming similar approaches using decision tree, support vector machine, neural network, Bayes net, naïve Bayes, and tree augmented naïve Bayes. Thus, the proposed approach shows promise for fast, accurate, and objective diagnosis of epilepsy in clinical settings.