Proposed System Framework Research Articles

Nonlinear MPPT techniques to control hybrid power systems

In recent years, grid-connected multifunctional photovoltaic (PV) systems have proven to be highly efficient. This system integrates PV panels with a DC–DC boost converter (DC–DC–BC) and the electrical distribution grid (DEG). Linking the PV to the AC-DEG is accomplished through a three-level multifunctional voltage source inverter (MVSI). The DC–DC–BC component in this study is engineered to perform maximum power point tracking (MPPT) irrespective of normal or abnormal conditions. The conventional MPPT technique poses several challenges and constraints on system usage. Hence, the suggestion is to adopt synergetic control (SC) and sliding mode control (SMC) to enhance the MPPT technique's performance within the proposed system framework. Moreover, predictive direct power control is applied to the MVSI-based shunt active power filter, utilizing a phase-locked loop technique to achieve multiple objectives: minimizing energy fluctuations, injecting active power, correcting power factors, compensating reactive power, and mitigating harmonic currents. To implement the proposed system, the MATLAB is used for this purpose, with several tests used to study the behavior of the controls proposed in this work. Numerical results indicate significant reductions in active and reactive power fluctuations, with estimated rates of 38.46% and 15.30%, respectively, compared to traditional strategies. Moreover, the total harmonic distortion (THD) of the source current after filtering is reduced by 31.88% under solar irradiation of G = 1000 Wm2. Before filtering, the THD of current experiences a reduction estimated at 97.65%. These findings underscore the superior performance of the proposed control technique across all evaluated aspects.

Three‐dimensional mapping and immersive human–robot interfacing utilize Kinect‐style depth cameras and virtual reality for agricultural mobile robots

AbstractNavigation and autonomous operation in farmland or greenhouse environments present significant challenges for agricultural mobile robots on account of unstructured scene conditions and complex task requirements. The immersive interfaces based on human–robot interaction have been extensively developed, which bridge the high‐level decision of human beings and the precise sensing and high motion ability of robots. In this study, we propose a human–robot interaction framework based on three‐dimensional mapping and virtual reality (VR) visualization. On the construction client, dense three‐dimensional point‐cloud maps are created based on the simultaneous localization and mapping real‐time appearance‐based mapping algorithm with a Kinect‐style depth camera. Subsequently, point‐cloud filtering and mesh creation methods are introduced for postprocessing the map models. A data communication network is employed to transmit the optimized models to the VR‐based exploration client. Utilizing VR visualization, the operator can gain an intuitive and comprehensive understanding of the environments to be explored. The three‐dimensional agricultural scenes are mapped into VR models through the system, which can combine the physical worlds with the VR spaces in a better manner. Compared with the video streaming‐based method for three‐dimensional mapping, the map models with colors and textures are entirely displayed in the VR headset, thus providing robust and global human–robot interaction interfaces and enhancing the immersive exploration for human beings to a considerable extent. Experimental results indicate that the three‐dimensional map models exhibit relatively sufficient construction integrity and yield favorable optimization effects. Additionally, according to a user questionnaire survey, immersive VR‐based interfaces can be more effectively utilized for the three‐dimensional mapping of mobile robots. Research evidence and results demonstrate that our proposed system framework offers positive assistance and support for enhancing human–robot interaction and executing specific robotic tasks in unstructured field agricultural environments. Code implementation and related data sets are shared at the link https://github.com/WangDongBUAA/Mapping_Interfacing.

Carbon allowance auction design of China's ETS: A comprehensive hierarchical system based on blockchain

This study proposes a blockchain-based optimization framework for carbon allowance allocation auctions. First, the study assesses the status quo of carbon trading, pinpointing barriers, future development trends, and identifying carbon quota auction standards and methods pertinent to the Chinese context. Second, based on China's carbon market needs and the technical characteristics of blockchain, this study reconfigures the functions and responsibilities of each participant and creates a framework for a carbon allowance auction system to optimize the carbon trading process. Third, an algorithm for the carbon allowance auction system is constructed, and the corresponding smart contract is created and simulated. The results illustrate that our proposed system framework facilitates universal carbon allowance auctions, efficiently addresses potential disputes during the auction process, enhances market transparency and credibility, and offers comprehensive privacy protection with flexible information disclosure options for corporations. Blockchain's role in fostering paid distribution and incentivizing participation in the carbon market is further recognized. A blockchain-based carbon trading system safeguards transaction security and efficiency, maintains precise transaction records, and provides automated settlement. Ultimately, this paper presents policy suggestions for the development of a national carbon market in China.

Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet

The sewer system plays an important role in protecting rainfall and treating urban wastewater. Due to the harsh internal environment and complex structure of the sewer, it is difficult to monitor the sewer system. Researchers are developing different methods, such as the Internet of Things and Artificial Intelligence, to monitor and detect the faults in the sewer system. Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects. However, the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small, which can affect the robustness of the model in the constraint environment. As a result, this paper proposes a sewer condition monitoring framework based on deep learning, which can effectively detect and evaluate defects in sewer pipelines with high accuracy. We also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer pipeline. This study modified the original RegNet model by modifying the squeeze excitation (SE) block and adding the dropout layer and Leaky Rectified Linear Units (LeakyReLU) activation function in the Block structure of RegNet model. This study explored different deep learning methods such as RegNet, ResNet50, very deep convolutional networks (VGG), and GoogleNet to train on the sewer defect dataset. The experimental results indicate that the proposed system framework based on the modified-RegNet (RegNet+) model achieves the highest accuracy of 99.5 compared with the commonly used deep learning models. The proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications.

Sentiment Analysis of Tweets During the COVID-19 Pandemic Using Multinomial Logistic Regression

Recently, the research on sentimental analysis has been growing rapidly. The tweets of social media are extracted to analyze the user sentiments. Many of the studies prefer to apply machine learning algorithms for performing sentiment analysis. In the current pandemic, there is an utmost importance to analyze the sentiments or behavior of a person to make the decisions as the whole world is facing lockdowns in multiple phases. The lockdown is psychologically affecting the human behavior. This study performs a sentimental analysis of Twitter tweets during lockdown using multinomial logistic regression algorithm. The proposed system framework follows the pre-processing, polarity and scoring, and feature extracting before applying the machine learning model. For validating the performance of proposed framework, other three majorly used machine learning based models-- namely decision tree, naïve Bayes, and K-nearest neighbors-- are implemented. Experimental results prove that the proposed framework provides improved accuracy over other models.

Performance Analysis of OPC UA for Industrial Interoperability towards Industry 4.0

Open Platform Communications Unified Architecture (OPC UA) incorporates a wide range of features and covers most of the requirements for a platform-independent interoperability standard which can be used to transmit data and information from the factory production floor to the enterprise and management level. Due to its highly scalable and interoperable architecture, it is well-positioned for future deployment in smart embedded devices towards Industry 4.0, especially in environments where there are heterogeneous communication nodes. In this paper, we aim to evaluate the performance of OPC UA for communication in Industrial Internet of Things (IIoT) environments to better understand the technical implementation of OPC UA and the feasibility of incorporating OPC UA directly to resource-constrained edge devices. We propose an architectural system framework for OPC UA performance evaluation across a wide range of experiments. Our experimental results demonstrated the efficacy of the proposed system and evaluation framework. The OPC UA-based IIoT system architecture and budget-friendly/cost-effective testbed setup can be flexibly adopted for protocol testing, prototyping and educational purposes.

Enabling Energy Trading in Cooperative Microgrids: A Scalable Blockchain-Based Approach With Redundant Data Exchange

Blockchain has recently been regarded as an important enabler for building secure energy trading in microgrid systems because of its inherent features of distributively providing immutable data record, storage, and sharing across networks in a peer-to-peer (P2P) manner. However, designing highly efficient and scalable blockchain-enabled energy trading mechanisms is extremely challenging because of the unique features of microgrid systems, e.g., bandwidth-constrained and high-latency communications and large-scale renewable energy source (RES) integration. To address this challenge, in this article, we propose a novel scalable blockchain-based energy trading framework for cooperative microgrid systems, which include four planes, i.e., data plane, consensus plane, smart plane, and application plane. Different from the existing solutions without consideration of network transmission, these four planes are designed with the capability of perceiving the status of block generation and transmission over interrupted P2P networks, and thus proactively improving the consensus process to guarantee the reliability of energy trading in cooperative microgrids. Meanwhile, built on this framework, a novel redundant data exchange strategy is proposed to improve the scalability of block creation with the presence of large-scale RES penetration and interrupted and dynamic communication links. Simulation results show that the proposed system framework outperforms the benchmark blockchain solutions. Furthermore, we investigate the potential applications of the proposed solutions in the practical microgrid systems to facilitate a clear understanding of the mechanisms of the proposed solutions.

A Secure and Traceable Vehicles and Parts System Based on Blockchain and Smart Contract.

As society advances, so does the total number of vehicles on the road, creating a massive consumer market for automobiles. According to statistics, a major portion of today’s traffic difficulties are caused by accidents caused by subpar cars and auto parts. As a result, each country has, over time, enacted equivalent rules and regulations to prevent such tragedies. However, in the face of profit, some people are desperate enough to employ illegal parts and illegally modified cars, and auto fraud is rampant. As a result, we employ the blockchain of the symmetrical Blockchain’s digital ledger and smart contract technology to build a decentralized supply chain system that can identify specific parts. In this study, we design and discuss the proposed system framework by user functions and the flow of parts based on blockchain, and we discuss communication protocols that use the symmetry and asymmetry cryptography, algorithms, properties, and security of the mechanism while providing related analysis and comparing the properties and costs of the system with other studies. Overall, the proposed method has the potential to successfully address the issue of automobile fraud.

Expressive Latent Feature Modelling for Explainable Matrix Factorisation-based Recommender Systems

The traditional matrix factorisation (MF)-based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This article introduces an MF-based explainable recommender system framework that utilises the user-item rating data and the available item information to model meaningful user and item latent features. These features are exploited to enhance the rating prediction accuracy and the recommendation explainability. Our proposed feature-based explainable recommender system framework utilises these meaningful user and item latent features to explain the recommendation without relying on private or outer data. The recommendations are explained to the user using text message and bar chart. Our proposed model has been evaluated in terms of the rating prediction accuracy and the reasonableness of the explanation using six real-world benchmark datasets for movies, books, video games, and fashion recommendation systems. The results show that the proposed model can produce accurate explainable recommendations.

Service Innovation and Quality Assessment of Industry 4.0 Microservice through Data Modeling and System Simulation Evaluation Approaches

This study proposes a system construction approach under Industry 4.0 infrastructure that is validated by the proposed framework of microservice quality assessment with framework with data modeling and simulation methodology to achieve innovation and value cocreation goals. The framework, which combines a dynamic process flow with service-dominant logic design and reliability assessment using a multilayer perceptron (MLP) prediction model can assist decision makers in optimizing their service innovation and decision-making processes. The service innovation and evaluation approaches have implications for optimizing the corporation cooperation. The corporation can form a much more comprehensive manufacturing infrastructure or system by considering the requirements and assessment results of third parties. To help the corporation redefine its value proposition and system structure, we must examine the system interaction between different hierarchical layers within the Industry 4.0 system infrastructure. This study used a production dataset from the NASDAQ-listed electronics corporation and two top German and Japanese automobile firms. The proposed system framework had already been validated and introduced to improve 12% of service quality. The system integrated with anticipated functions will accelerate service innovation and optimization by combining MLP and Kaplan–Meier estimation methodologies by extracting the characteristics of realistic datasets.

An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology. Image retrieval has become one of the vital tools in image processing applications. Content-Based Image Retrieval (CBIR) has been widely used in varied applications. But, the results produced by the usage of a single image feature are not satisfactory. So, multiple image features are used very often for attaining better results. But, fast and effective searching for relevant images from a database becomes a challenging task. In the previous existing system, the CBIR has used the combined feature extraction technique using color auto-correlogram, Rotation-Invariant Uniform Local Binary Patterns (RULBP) and local energy. However, the existing system does not provide significant results in terms of recall and precision. Also, the computational complexity is higher for the existing CBIR systems. In order to handle the above mentioned issues, the Gray Level Co-occurrence Matrix (GLCM) with Deep Learning based Enhanced Convolution Neural Network (DLECNN) is proposed in this work. The proposed system framework includes noise reduction using histogram equalization, feature extraction using GLCM, similarity matching computation using Hierarchal and Fuzzy c- Means (HFCM) algorithm and the image retrieval using DLECNN algorithm. The histogram equalization has been used for computing the image enhancement. This enhanced image has a uniform histogram. Then, the GLCM method has been used to extract the features such as shape, texture, colour, annotations and keywords. The HFCM similarity measure is used for computing the query image vector's similarity index with every database images. For enhancing the performance of this image retrieval approach, the DLECNN algorithm is proposed to retrieve more accurate features of the image. The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy, precision, recall, f-measure and lesser complexity. From the experimental results, it is clearly observed that the proposed system provides efficient image retrieval for the given query image.

C4I System Security Architecture: A Perspective on Big Data Lifecycle in a Military Environment

Although the defense field is also one of the key areas that use big data for security reasons, there is a lack of study that designs system frameworks and presents security requirements to implement big data in defense. However, we overcome the security matters by examining the battlefield environment and the system through the flow of data in the battlefield. As such, this research was conducted to apply big data in the defense domain, which is a unique field. In particular, a three-layered system framework was designed to apply big data in the C4I system, which collects, manages, and analyzes data generated from the battlefield, and the security measures required for each layer were developed. First, to enhance the general understanding of big data and the military environment, an overview of the C4I system, the characteristics of the 6V’s, and the five-phase big data lifecycle were described. While presenting a framework that divides the C4I system into three layers, the roles and components of each layer are described in detail, considering the big data lifecycle and system framework. A security architecture is finally proposed by specifying security requirements for each field in the three-layered C4I system. The proposed system framework and security architecture more accurately explain the unique nature of the military domain than those studied in healthcare, smart grids, and smart cities; development directions requiring further research are described.

Customer satisfaction service match and service quality-based blockchain cloud manufacturing

Blockchain cloud manufacturing is an emerging service-oriented paradigm that takes many advantages to cloud manufacturing. Previous researchers have studied from eradicating third party trust problem among service providers and customers by decentralized data record and involving smart contract. However, the existing paradigm still suffers from third party trust problem among service-related participants and cloud manufacturing provider since service is solely managed by cloud manufacturing provider without supervision and service related participants cannot know whether composed service provision which indeed is NP-hard problem is optimal. In this paper, the architecture of blockchain cloud manufacturing system is proposed with adoption of several cloud manufacturing providers and vote mechanism to vote for optimal composed service provision on candidate composed service scheme firstly calculated by one cloud manufacturing provider. According to customer satisfaction-based service match and quality control, a system framework of composed service performance evaluation is desifned to incorporate the vote mechanism. Firstly, customer satisfaction dimensions as evaluation dimensions are extracted from customer text reviews based on latent dirichlet allocation (LDA) and sub-dimensions under each customer satisfaction dimension are set up based on professional field knowledge. Secondly, overall customer satisfaction rating of reviews and sentiment of reviews towards customer satisfaction dimensions are analysed by three score scheme, bag of words (BOW) and support vector machine (SVM) classification. Thirdly, the sentiment effect and category of customer satisfaction dimension on overall customer satisfaction are identified with the application of neural network and Kano model. Fourthly, in consideration of category characteristic, overall performance of customer satisfaction dimension is measured as distance and using proposed Logit-SVM method. Service selection standard according to value of customer satisfaction is set up. Finally, one empirical study is presented to verify the feasibility of proposed system framework, and proposed model is further discussed.

Digital Twin-Based Ecogreen Building Design

The high operation cost of green building, insufficient informationization and automation management capability, and the lack of effective operation cost control seriously restrict the development of the industry and the realization of green goals. In order to solve the problem of insufficient capability of green building operation cost management, based on the digital twin technology in the manufacturing field, we analyze the characteristic requirements and theoretical basis of green building operation cost management for system, propose a green building operation cost management system based on digital twin, and refine the design of each structural layer of this system. It is necessary to set up a series of lines, although it takes a certain amount of time. There are four types of applications, namely, the number of types of applications, the completion of the effective number, the comparison of functions, and the implementation capabilities. The study shows that the proposed system framework can improve the efficiency and quality of green building operation cost management through technology upgrade and process optimization. The implementation of digital twin and human-machine collaboration is an advanced stage in the development of digital architecture because virtual things and real things, materials, and numbers are mutually promoting processes. The inspiration of this technological view for architecture is that digital twin and human-machine collaboration not only allow the interaction between virtual and reality and emphasize the feedback of actual construction to virtual simulation but also promote a kind of mutual promotion of human and machine thinking and construction ability.

A Context Aware Job Information Real-Time Delivery Framework for Pervasive Environment

Context-aware systems are getting rapid popularity towards enabling a pervasive computing environment over hand-held mobile devices, wearable devices, and Tablets. These systems deliver services tailored to the specific needs and user’s context. The context-aware system services can utilize information about the user’s context to adapt services based on the user’s location. The prime aim of this research paper is to investigate and analyse the critical issues and challenges while delivering the job information to the job seekers when they migrate from one place to another in search of jobs. In addition, the research also tries to investigate the challenges faced by employers/ job providers when they search and recruit the right person for the right jobs. In this paper the issues and challenges investigated and analysed were timeliness of information dissemination, opportunity missing, fake/genuine information, anywhere, anytime accessibility over small handheld devices. After rigorous analysis of the investigated issues and challenges in the existing state of art employment/job information dissemination systems, a real-time context-aware employment information delivery system framework for the pervasive environment is proposed. The research parameters considered are Preference of Job seekers, Location of Jobs, Context, Qualification, CGPA, Experience, everywhere access at any time, and Identity. The paper used both the quantitative and qualitative approaches for proposing a research-based applied solution. Survey of job seekers, Interview of recruiters, and technical observation of the researcher are used for collecting the relevant facts to check the researchability of the issues and challenges. Protégé version 4.3 is used for modelling, Edraw-Max for designing the framework, Google Form for Survey, and Justin mind tool for Prototype design and testing. The framework and its Prototype was examined using a user acceptance test. The acceptance test indicated that 84.6% of target stakeholders accepted the proposed system framework as a new knowledge solution for the aforementioned issues and challenges in the employment sectors

A Reliable and an Efficient Approach for Diagnosis of Brain Tumor Using Transfer Learning

Introduction: The World Brain Tumor Day is seen on eighth June, in a year. Despite exhaustive research in the medical field, the prevalence of this deadly disease is increasing globally with over new 28,000 braintumor cases being reported annually, in India alone. Recent advancements in the field of machine learning facilitate minimally invasive, efficient and reliable procedures for the diagnosis of Brain tumor. Objective: This research intends to design and devlop a reliable framework for accurate diagnosis of brain tumor mainly meningioma type, gliomatype and pituitary cerebrum tumor utilizing Magnetic Resonance Imaging (MRI), one of the most mainstream non-obtrusive procedure Methods: In the proposed system, pre-trained AlexNet is used to classify meningioma, glioma and pituitary brain tumor. The concept of transfer learning is applied using AlexNet for extracting the features from brain MRI images.The AlexNet contains eight layers in which the first five are convolution layer and the remaining three are fully connected layers. The last layer is a softmax layer which gets the output from fully connected layers. The ReLU non-linearity is applied to the output of every convolution and fully connected layer. The idea of transfer learning is applied utilizing AlexNet for computing thefeatures from brain MRI pictures. The AlexNet contains eight layers in which the initial five are convolution layers and the staying three layers are fully connected layers. a softmax is the last layer , which is feeded by the fully connected layers. The ReLUactivation function is applied to the output of convolution layer and fully connectedlayer Result: The proposed system framework recorded the best order precision of 100 % to classify the brain tumor when validated using a practical dataset. Conclusion: The proposed work presents accurate and automatic brain tumor classification using transfer learning. The features extracted using AlexNet has proven to be efficient in obtaining good discriminative power in diagnosis of brain tumor

Mining top-N high-utility operation patterns for taxi drivers

In recent years, the rapid development of mobile network and wireless sensor technology has brought opportunities to change the way of the existing taxi business operation. How to improve the operation revenues of taxi drivers has become a topic worthy of research. This paper analyzes and mines taxi operation data to provide taxi drivers with personalized sequence recommendation services, thereby increasing their expected revenues. Different from previous works, the proposed method in this paper recommends a series of future operation orders for taxi drivers, instead of recommending several discrete locations for the current order. In this paper, firstly, by performing spatial-temporal clustering on the origins and destinations of passengers, the spatial and temporal distribution characteristics of passengers in the city are identified. Secondly, the origin of the current passenger is used as the root node to construct a top-N high-utility sequence tree, and this process can be divided into two processes: top-down building tree and bottom-up sorting path utility. The two pruning strategies of node utility and path utility are used to reduce the generation of candidate sets. Finally, a series of potential orders based on dynamic context are recommended to taxi drivers, so as to maximize the expected revenues of taxi drivers. The experimental results demonstrate that there is a close relationship between taxi drivers’ operation behavior patterns and their revenues. The proposed system framework and algorithm in this paper can effectively mine global and long-term top-N high-utility operation patterns.

Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms

Mathematical modeling of language in Artificial Intelligence is of the utmost importance for many research areas and technological applications. Over the last decade, research on text representation has been directed towards the investigation of dense vectors popularly known as word embeddings. In this paper, we propose a cognitive-emotional scoring and representation framework for text based on word embeddings. This representation framework aims to mathematically model the emotional content of words in short free-form text messages, produced by adults in follow-up due to any mental health condition in the outpatient facilities within the Psychiatry Department of Hospital Fundación Jiménez Díaz in Madrid, Spain. Our contribution is a geometrical-topological framework for Sentiment Analysis, that includes a hybrid method that uses a cognitively-based lexicon together with word embeddings to generate graded sentiment scores for words, and a new topological method for clustering dense vector representations in high-dimensional spaces, where points are very sparsely distributed. Our framework is useful in detecting word association topics, emotional scoring patterns, and embedded vectors’ geometrical behavior, which might be useful in understanding language use in this kind of texts. Our proposed scoring system and representation framework might be helpful in studying relations between language and behavior and their use might have a predictive potential to prevent suicide.

Towards Deriving Freight Traffic Measures from Truck Movement Data for State Road Planning: A Proposed System Framework

To make the right decisions on investments, operations, and policies in the public road sector, decision makers need knowledge about traffic measures of trucks, such as average travel time and the frequency of trips among geographical zones. Private logistics companies daily collect a large amount of freight global positioning system (GPS) and shipment data. Processing such data can provide public decision makers with detailed freight traffic measures, which are necessary for making different planning decisions. The present paper proposes a system framework to be used in the research project “A new system for sharing data between logistics companies and public infrastructure authorities: improving infrastructure while maintaining competitive advantage”. Previous studies ignored the fact that the primary step for delivering valuable and usable data processing systems is to consider the final user’s needs when developing the system framework. Unlike existing studies, this paper develops the system framework through applying a user-centred design approach combining three main steps. The first step is to identify the specific traffic measures that satisfy the public decision makers’ planning needs. The second step aims to identify the different types of freight data required as inputs to the data processing system, while the third step illustrates the procedures needed to process the shared freight data. To do so, the current work employs methods of literature review and users’ need identification in applying a user-centralized approach. In addition, we develop a systematic assessment of the coverage and sufficiency of the currently acquired data. Finally, we illustrate the detailed functionality of the data processing system and provide an application case to illustrate its procedures.

GBDT-Based Fall Detection with Comprehensive Data from Posture Sensor and Human Skeleton Extraction.

Since fall is happening with increasing frequency, it has been a major public health problem in an aging society. There are considerable demands to distinguish fall down events of seniors with the characteristics of accurate detection and real-time alarm. However, some daily activities are erroneously signaled as falls and there are too many false alarms in actual application. In order to resolve this problem, this paper designs and implements a comprehensive fall detection framework on the basis of inertial posture sensors and surveillance cameras. In the proposed system framework, data sources representing behavior characteristics to indicate potential fall are derived from wearable triaxial accelerometers and monitoring videos of surveillance cameras. Moreover, the NB-IoT based communication mode is adopted to transmit wearable sensory data to the Internet for subsequent analysis. Furthermore, a Gradient Boosting Decision Tree (GBDT) classifier-based fall detection algorithm (GBDT-FD in short) with comprehensive data fusion of posture sensor and human video skeleton is proposed to improve detection accuracy. Experimental results verify the good performance of the proposed GBDT-FD algorithm compared to six kinds of existing fall detection algorithms, including SVM-based fall detection, NN-based fall detection, etc. Finally, we implement the proposed integrated systems including wearable posture sensors and monitoring software on the Cloud Server.