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Bluetooth has solidified its status as one of the most prominent and versatile wireless communication technologies alongside Wi-Fi. In spite of its widespread usage, contemporary Bluetooth testing technology still has many unaddressed shortcomings and issues. Hardware limitations, computational complexity, and the lack of standardization are some of these longstanding problems. Furthermore, very few systems possess a proven and reliable capability to test both Wi-Fi and Bluetooth. This paper presents the development of an adaptive Bluetooth test-bed framework that can not only overcome limitations with existing systems but can accommodate other technologies and protocols as well. The work presented in this paper includes: a packet processing and logging utility, a promiscuous packet capture subsystem built around a production test engine, and a complementary subsystem that handles encryption and other security mechanisms.

This paper reports the performance of link setup time reduction outlined by IEEE 802.11ai, which is also known as Fast Initial Link Setup (FILS), in real intermittent inter-vehicular communications. Fast link establishment is a significant concern in communications between mobile devices with high mobility, such as passing vehicles on roads, because short link setup times enable vehicles to transfer larger amounts of application data. However, secure links are also important because they prevent both unauthorized access by unauthenticated users and misinformation circulation by malicious persons. Conventional security protocols such as IEEE 1609.2 and IEEE 802.11i/IEEE 802.1X, which is also known as theWi-Fi Protected Access 2 Extensible Authentication Protocol (WPA2-EAP), archive user authentication in vehicular networks but often take several seconds to establish a secure link due to numerous frame exchanges. In contrast, FILS is designed to establish a secure WPA2-EAP link in 100 ms using cached authentication information. However, since the effectiveness of FILS in real vehicular networks has not yet been reported, this paper describes experiments that clarify its setup time reduction abilities in 2.4 GHz IEEE 802.11n-based inter-vehicular communications by measuring the initial link setup times between two passing vehicles in a real environment. The results show that FILS significantly reduces the initial link setup times between the passing vehicles to around 150 ms and increases the size of application data transferred between vehicles. Additionally, it is demonstrated that FILS always establishes a secure link, while Protected EAP (PEAP) sometimes fails. Finally, in communications between vehicles passing each other at the relative speed of 80 km/h, we confirm that the FILS link setup time reduction effectively increases transferrable application data sizes by 10MB compared with WPA2-PEAP.

The logistics industry and manufacturing industry are naturally closely linked. Analysis of the interaction mechanism between the logistics industry and manufacturing industry based on two different linkage types of outsourcing logistics and self-owned logistics is helpful to deepen the understanding of the relationship between these two industries. This paper performs a comparative analysis of the different linkage modes represented by the three northeast provinces and the Yangtze River Delta region in China. First, this paper analyzes the interactive influence mechanism of the logistics industry efficiency and manufacturing industry efficiency. Second, the adjustment mechanism of the logistics industry to the manufacturing industry under the two modes is analyzed according to the different logistics organization modes of the manufacturing industry. Finally, we build an efficiency measurement model considering the joint efficiency of the economy and environment. Using the panel vector autoregression (PVAR) model, this paper empirically compares and analyzes the interaction between the logistics industry and manufacturing industry under different linkage modes. The results show the following: (1) In different economic development areas, the interaction between logistics and manufacturing efficiency is not equal. (2) According to the different logistics methods of the manufacturing industry, there are self-owned modes and logistics outsourcing modes in the linkage development of the logistics industry and manufacturing industry. There are great differences in the mode and degree of action between the two modes. (3) The interactive relationship between logistics industry and manufacturing industry under different linkage modes is totally different, and the direction and intensity of the impact are also completely different.

Dyslexia is a neurological disorder that is characterized by imprecise comprehension of words and generally poor reading performance. It affects a significant population of school-age children, with more occurrences in males, thus, putting them at risk of poor academic performance and low self-esteem for a lifetime. The long-term hope is to have a dyslexia diagnostic method that is informed by neural-biomarkers. In this regard, large numbers of machine learning methods and, more recently, deep learning methods have been implemented across various types of dataset with the above-chance classification accuracy. However, attainment of clinical acceptability of these state-of-the-art methods is bedeviled by certain challenges including lack of biologically-interpretable biomarkers, privacy of dataset and classifiers, hyper-parameter selection/optimization, and overfitting problem among others. This review paper critically analyzes recent machine learning methods for detecting dyslexia and its biomarkers and discusses challenges that require proper attentions from the users of deep learning methods in order to enable them to attain clinically relevance and acceptable level. The review is conducted within the premise of implementation and experimental outcomes for each of the 22 selected articles using the Preferred Reporting Items for Systematic review and Meta-Analyses (PRISMA) protocol, with a view to outlining some critical challenges for achieving high accuracy and reliability of the state-of-the-art machine learning methods. As an evidence-based protocol for reporting in systematic reviews and meta-analyses, PRISMA helps to ensure clarity and transparency of this paper by showing a four-phase flow diagram of the selection process for articles used in this review. It is therefore, envisaged that higher classification performance of clinical relevance can be achieved using deep learning models for dyslexia and its biomarkers by addressing identified potential challenges.

Several hypovigilance detection systems (HDx) were developed to avoid road-side accidents due to driver fatigue. They have suffered from several limitations. Notably many of these are focused on center-head position to define an area of interest (often referred to as PERCLOS (percentage eye closure)) without considering the face occlusion problem, light illumination, and suffer poor response time. These HDx systems mostly depend on image processing, vision-based, and multisensor-based features. To address these problems, the author utilized vision, sensors, environmental, and vehicular-based features that integrated together by fusion to predict multistage of HDx. Lately, few studies have utilized the combination of multimodal features and deep learning (DL) architectures. Those multimodal-based features (M-HDx) were feasible to predict stages of driver fatigue (multi-stage). However, there is a need to critically measure the performance of these M-HDx by carrying out a comparative analysis to recognize multi-stage of fatigue in terms of hardware-based benchmarks. Moreover, it is important to evaluate the M-HDx systems using different features-set with respect to traditional and advanced machine learning techniques. Therefore, the primary aim of this work is in algorithm and feature modeling, then compare the advantages and differences with other work. In this paper, a different study is conducted compare to state-of-the-art survey articles by statistically measuring the performance. After experiments on M-HDx systems, this paper concludes that there is still a research gap to real-time development of multistage M-HDx systems. In the end, the paper summarizes the directions, challenges, and applications in the development of HDx systems to assist other researchers for further research.

This article mainly studies Electroencephalogram (EEG) mental recognition. Because the human brain is very complex and the EEG signal is greatly affected by the environment, EEG mental recognition can be attributed to domain adaptative problems. Our main work is as follows: (1) At present, most domain adaptation learning only learns the linear subspace of Reproducing Kernel Hilbert Space (RKHS), and RKHS itself does not. Given the complexity and nonlinearity of EEG mental recognition, we propose an EEG mental recognition algorithm based on two learning. The two learning is RKHS learning and RKHS subspace learning. The source dictionary regularized RKHS subspace learning we proposed applies to EEG mental recognition and is better than pure RKHS subspace learning, but not enough. To get satisfactory results, we learn RKHS before RKHS subspace. (2) According to Moore–Aronszajn theorem, RKHS can be uniquely generated by a kernel function. The existing RKHS is rarely learnable. It is difficult to find a kernel function that can be learned and optimized. In RKHS learning, this paper uses a learnable kernel function that we published, and the kernel function is easy to optimize. (3) In RKHS subspace learning, most of the existing methods adopt the Maximum Mean Discrepancy (MMD) criterion, but it cannot make the spatial distribution of the same category of source and target domain data overlap as much as possible, and the label of the target domain data is unknown. To solve this problem, this paper proposes a framework of RKHS subspace learning based on source domain dictionary regularization. The experimental results on the brain-computer interface international competition data set (BCI competition IV 2a) show that the effect of the algorithm proposed in this paper is better than that of the other five advanced domain adaptation learning algorithms.

This paper presents a bibliometric assessment of the Software Engineering (SE) community in the Kingdom of Saudi Arabia (KSA). The assessment was based on the number of SE papers published by KSA-based SE researchers in SE-related venues and indexed in Scopus between years 1984-2019. The assessment aimed to measure the volume of research contribution produced by KSA-based researchers and institutions to the SE field. 802 SE papers were published by KSA-based SE researchers and the top active institution in the domain is King Fahd University of Petroleum and Minerals (KFUPM) and the top active researcher is Mohammad Alshayeb who is affiliated to KFUPM. The results estimated that KSA produced around %0.62 of the world-wide SE knowledge which indicates that KSA SE community needs to increase the volume of its publications to be more active in the worldwide SE community. The results also show that from 2007 onwards, the annual publication trend of KSA SE community has been growing in a healthy rate reaching 113 published papers in 2019. Additionally, 56% of the papers were internationally-authored and the highest international collaborations were with researchers from the USA, Tunisia and the UK respectively. In general, KSA is performing well comparing to three of its neighboring countries (UAE, Jordan and Egypt) in terms of the quantity of the published SE papers and received citations. However, in comparison to developed countries, the results suggest that more work on the quantity, quality and visibility of the SE papers authored by KSA-based researchers is needed.

In this paper, a new application of Equilibrium Optimizer (EO) is proposed for design hybrid microgrid to feed the electricity to Dakhla, Morocco, as an isolated area. EO is selected to design the microgrid system due to its high effectiveness in determining the optimal solution in very short time. EO is presented for selecting the optimal system design which can minimize the cost, improve the system stability, and cover the load at different climate conditions. Microgrid system consists of photovoltaic (PV), wind turbine (WT), battery, and diesel generator. The objective function treated in this paper is to minimize the net present cost (NPC), respecting several constraints such as the reliability, availability, and renewable fraction. The sensitivity analysis is conducted in two stages: Firstly, the impact of wind speed, solar radiation, interest rate, and diesel fuel on the NPC, and levelized cost of energy (LCOE) is analyzed. Secondly, the influence of size variation on loss of power supply probability (LPSP) is investigated. The results obtained by EO are compared with those obtained by recent metaheuristics optimization algorithms, namely, Harris Hawks Optimizer (HHO), Artificial Electric Field Algorithm (AEFA), Grey Wolf Optimizer (GWO), and Sooty Tern Optimization Algorithm (STOA). The results show that the optimal system design is achieved by the proposed EO, where renewable energy sources (PV and WT) represent 97% of the annual contribution and fast convergence characteristics are obtained by EO. The best NPC, LCOE, and LPSP are obtained via EO achieving 74327 $, 0.0917 $/kWh, and 0.0489, respectively.

Leveraging the edge computing paradigm, computing resources are deployed in the network edge to provide heterogeneous services. Edge computing delivers sensing and actuating services to the Internet from the constrained Internet of Things (IoT) devices. Meanwhile, management of various elements is provided by offloading sufficient computing and storage to the edge of the networks for the IoT environments such as home, factory, and private spaces without cloud servers. In this paper, we propose an enhanced service framework based on microservice management and client support provider for efficient user experiments in the edge computing environment. For providing the edge computing service and management in the network edge, this paper presents an edge-computing architecture that provides various functions through microservice modules on the edge platform engine. Through the microservices, the interfaces are provided to the client to access the device, data, and additional services. Using Docker, the microservice modules are deployed in the edge platform to provide the services. However, the services and management functions need to be presented to the clients based on the friendly user interfaces. For providing the user interfaces of the services and Docker engine to the clients, the client support service provider is developed and deployed in the network edge based on the edge platform. Therefore, the proposed edge platform provides the services and management to the users for accessing the resources and functions through visualized interfaces in the IoT environment based on edge computing. The performance of our proposed system can be checked through the test result screen and delay time. Compared to controlling edge computing by using a command-line tool for users, we made it easy for general users who are not computer savvy to access edge services through a graphic user interface. And by measuring the delay time and comparing the execution time, it can be seen that the proposed system operates faster.

The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-to-end behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.

Medical files can help people prevent diseases, increase cure rates, promote medical development and help solve major public health crises. However, medical files are strongly private. It is an urgent problem needed to be solved that how to share medical files with privacy and data security. The existing models based on the centralized certificate authority is a feasible method, but it is possible to experience a single point failure. Besides there is a mismatch between the models and real-life scenario since they are only suitable for single patient. Therefore, this paper proposes a practical medical file sharing scheme based on blockchain and decentralized attribute-based encryption. The blockchain is used to record application and grant of authorizations. Smart contracts provide an interactive platform for all users in the system. By utilizing decentralized attribute-based encryption, fine-grained access control of medical files is carried out to ensure privacy and security as well as avoiding single point failure. Attribute-based algorithm that support multi-person democratic decision making and dynamic personnel changes are designed to make the model much closer to the real scene. Finally, through security, performance and comparing analysis with other solutions, the scheme in this paper can meet the needs of real-life scenarios in terms of security and practicability, and provides a new practical model for medical file sharing.

This paper analyzes the potential impact of implementing demand response strategies in a power system. This work aims to present a methodology to evaluate three demand response models to reduce frequency variations in the system. The method starts with the modeling of the system load and the demand response strategies. The power loads are modeled through active power and reactive power measurements in the system’s different buses. A data-driven methodology is proposed to obtain three profiles that simulate residential, commercial, and industrial users’ behavior. Mathematical modeling is proposed for demand response strategies. Time of Use tariff, Solar PV Distributed Generation, and Load Curtailment are the strategies used for residential, commercial, and industrial users, respectively. A brand-new combination of scenarios is developed in this paper with different penetration levels of the demand response strategy. Besides, a novel analysis of the frequency profile is performed for the proposed scenarios. A modified IEEE-39 power system is proposed, adjusting generation and demand using the Colombian demand profile and the generating units’ energy mix. The results indicate that the implementation of demand response strategies improves the system’s frequency profile. The frequency drop was reduced by 11.4 %, and power generator units released up to 2.1 GWh through the day with the implementation of the DR strategies.

To improve the fusion quality of infrared and visible images and highlight target and scene details, in this paper, a novel infrared and visible image fusion algorithm is proposed. First, a method for combining dynamic range compression and contrast restoration based on a guided filter is adopted to enhance the contrast of visible source images. Second, guided filter-based image multiscale decomposition is used to decompose images into base layers and detail layers. For base layer fusion, a fusion strategy based on the detail and energy measurements of the source image is proposed to determine the pixel value of the fused image base layer such that the energy loss of the fusion can be reduced and the texture detail features are highlighted to obtain more source image details. Finally, recursive separation and weighted histogram equalization methods are applied to optimize the fused image. Experimental results show that the fusion algorithm and fusion strategy proposed in this paper can effectively improve fusion image clarity, while more detailed target and scene information can still be retained.

Clustering algorithms have become one of the most critical research areas in multiple domains, especially data mining. However, with the massive growth of big data applications in the cloud world, these applications face many challenges and difficulties. Since Big Data refers to an enormous amount of data, most traditional clustering algorithms come with high computational costs. Hence, the research question is how to handle this volume of data and get accurate results at a critical time. Despite ongoing research work to develop different algorithms to facilitate complex clustering processes, there are still many difficulties that arise while dealing with a large volume of data. In this paper, we review the most relevant clustering algorithms in a categorized manner, provide a comparison of clustering methods for large-scale data and explain the overall challenges based on clustering type. The key idea of the paper is to highlight the main advantages and disadvantages of clustering algorithms for dealing with big data in a scalable approach behind the different other features.

Plane block parallel blocking flowline is a special parallel blocking flowline, which can also be called incomplete hybrid flowline. However, the existing scheduling work has less research on this problem and lacks the constraint of considering the deterioration effect. Therefore, this paper studies the plane block parallel blocking flowline scheduling problem considering the deterioration effect (PBFSP-DE). This is a typical NP hard problem, so this paper proposes a Hybrid Whale Optimization Algorithm (HWOA) for this problem. In the HWOA, tent map of chaotic map is introduced to initialize the population, to improve the diversity of the initial population and effectively improve the optimization performance of the algorithm. Then, a hybrid strategy combining adaptive weight factor and Gaussian random disturbance is used to update the whale’s position, to provide strong global exploration ability and avoid falling into local optimum. In addition, Levy flight mechanism is applied to the current optimal individuals to improve the efficiency of local search. Finally, the results show that the HWOA proposed in this paper has strong engineering practicability.

Probabilistic forecasts of electrical loads and photovoltaic generation provide a family of methods able to incorporate uncertainty estimations in predictions. This paper aims to extend the literature on these methods by proposing a novel deep-learning model based on a mixture of convolutional neural networks, transformer models and dynamic Bayesian networks. Further, the paper also illustrates how to utilize Stochastic Variational Inference for training output distributions that allow time series sampling, a possibility not given for most state-of-the-art methods which do not use distributions. On top of this, the model also proposes an encoder-decoder topology that uses matrix transposes in order to both train on the sequential and the feature dimension. The performance of the work is illustrated on both load and generation time series obtained from a site representative of distributed energy resources in Norway and compared to state-of-the-art methods such as long-short-term memory. With a single-minute prediction resolution and a single-second computation time for an update with a batch size of 100 and a horizon of 24 hours, the model promises performance capable of real-time application. In summary, this paper provides a novel model that allows generating future scenarios for time series of distributed energy resources in real-time, which can be used to generate profiles for control problems under uncertainty. INDEX TERMS deep learning, generation forecasting, load forecasting, neural networks, probabilistic methods, renewable power

During the last decade there has been a major shift towards renewable energy sources to fulfill the increasing demand for energy in a sustainable manner. However, a major challenge with renewable energy generation is its dependency on weather conditions. Energy storage is deemed instrumental to harness renewable energy by providing a means to overcome stochasticity in renewable generation. Nonetheless, the operation of energy storage is not trivial due to its energy limitation and degradation behavior. Many works in literature consider forecasts as a cornerstone for effective management of energy storage for various grid applications. However, little work has been devoted to studying the actual value of forecast for energy storage management, which is highly dependent on the use case. This paper presents a review of the state of the art in the use of forecasts for energy storage management, identifying the estimated value of forecast with respect to baseline management approaches that do not rely on forecasts. The paper also discusses research pathways that would focus on improving forecast only on the energy storage applications that can benefit from it.

In the field of Compressed Sensing, estimation of sparsity level is very essential as the sparsity level determines the minimum number of (i) measurements to be obtained of a sparse signal during acquisition and (ii) iterations to be performed for many of the greedy techniques for the perfect recovery of the sparse signal from the obtained measurements. In this paper, we propose a Maximum Likelihood (ML) estimator to estimate the instantaneous sparsity level during acquisition and an ML sequence (MLS) estimator of sparsity levels during recovery. As the sparsity level varies in time due to the continuous birth of newer supporting components and death of existing supporting components, this paper models the sparsity level variation as a stochastic birth-death process. The real-world applications of the proposed estimators are presented on the compression of aircraft vibration signals and the estimation of wireless channels. The simulation results on real-world and model-generated data demonstrate the performance merits of the proposed estimators compared to other existing methods.

Under China’s vigorous development of integrated energy services, the Integrated Energy Service Agency (IESA) is responsible for purchasing energy from external markets and selling energy to multi-energy users (MEUs). Currently, an increase in the various forms of energy in industrial parks has caused great uncertainty for MEUs participating in an integrated demand response (IDR) but has also provided an opportunity for industrial parks to optimize energy conservation. Therefore, determining how to build an elastic energy cloud model with IDR uncertainty and add the uncertainty and randomness of the cloud model to the optimal scheduling of an industrial park integrated energy system is a key problem. In this paper, an optimal economic dispatch model of an industrial park is proposed and considers the uncertain elastic energy of IDR. In this model, the IESA is responsible for the reasonable scheduling of equipment for optimal operation, the establishment of integrated energy retail prices for MEUs, and the goal of maximizing the net income of the IESA. First, the functional relationship among the self-elastic coefficient, retail energy prices, and IDR variation is considered. A cloud model of the self-elastic coefficient is constructed to indirectly represent the multiple uncertainties of the elastic energy in the industrial park. Second, this paper compares and analyzes the economic benefits and IDR potential of the industrial park by considering only single power users in different intervals and the selection of cloud drop elements of MEUs in all intervals. Finally, a new scene random sampling method based on interval contributions (SRS-IC) is employed to solve the optimization model, and a typical example is used to demonstrate that the model and method can guarantee the overall economy of the industrial park, improve the computational efficiency, and explore the IDR potential of MEUs.

Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

© Copyright 2021, A. O. Nyanteh et al. Software-Defined Networking (SDN) is an innovative technology which provides a programmable network control which is decoupled from the physical infrastructure. Network Virtualization (NV) is the phenomenon where a given physical network infrastructure and its resources are abstracted to create multiple logical virtual network slices of the underlying substrate. NV enables independent virtual networks to co-exist on one or more shared physical network infrastructure. Edge computing makes use of the edge resources in close proximity to end-users to reduce service delay and the network traffic volume in the end-to-end networks. Similarly, network slicing which is a key enabling technology for 5G networks is designed to support different services from different platforms at different scales enables sharing of physical network infrastructure on many different virtual network layers. These innovative technologies and strategies have gained significant attention from both academia and industry as they have the potential to maximize network resource utilization and optimize end–to–end network service delivery in 5G solutions deployment. To enable continuous simulation and development of applicable 5G networking concepts using these technologies, there is a need for an accessible and easy-to-learn testbed which is able to efficiently measure the performance of physical and virtual network capacities, provisioning approaches and management of multiple architectural models using large-scale network slicing configurations in a repeatable and controllable manner. These tools and toolkits provide scalable, lightweight and controlled cloud simulation environments necessary to analyse network traffic flows, allocation capacities and policies and the behaviour of multiple heterogeneous networks at an extremely low cost as compared to the huge financial commitments involved in conducting similar experiments in a real-life event. Existing solutions do not support Network Slicing and end- to -end heterogenous network automation which are key enablers of 5G network implementation. Hence in this paper, the CloudSimHypervisor framework is developed in this based on CloudSimSDN-NFV. The complete architecture and features of the CloudSimHypervisor framework and some used cases are presented in this paper. We validate the CloudSimHypervisor framework with two use case experiments in the cloud computing environment: Joint compute and network resource utilization and network traffic prioritization. Results from these experiments display the efficiency of the CloudSimHypervisor in estimating and measuring processing speed, transmission speed, compute and network usage efficiency and energy consumption.

Wireless charging is a convenient method of charging Electric Vehicles (EVs). Its application has been widely studied for electric cars but so far there have been limited experiments for electric bicycles (e-bikes). This paper addresses the design process and implementation details related to a wireless charger for a specific e-bike. Some issues such as the position of the secondary coil or the control algorithm prove to be crucial to the performance of the wireless transfer. Concerning the placement of the secondary coil, the paper proposes and validates a theoretical model to quantify the effects of the surrounding interfering materials. A CC-CV (Constant Current - Constant Voltage) control algorithm has been implemented to validate the theoretical results in these two charging phases. The control, the coils and the compensation networks have been designed to be simple and robust. The theoretical model, the charger and its control have proven to be effective in a real 84-W e-bike.

This paper comments on an article published in IEEE Access (C. Zhou et al. , “An Adaptive Terrain-Dependent Method for SRTM DEM Correction Over Mountainous Areas,” IEEE Access, Vol. 8, pp. 130878–130887, 2020, DOI: 10.1109/ACCESS.2020.3009851) that presented a method for correcting the Shuttle Radar Topography Mission (SRTM) digital elevation data product. The present comments concern the proposed method and the data used by the authors of that paper. First, Zhou et al. ignored a fundamental accuracy limit of any digital elevation model (DEM) imposed by elevation data’s discretization and quantization operations. These limits are intrinsic to DEMs and cannot be overcome. The current paper presents a detailed mathematical model of these limits. Second, Zhou et al. used GPS stations’ elevation as a reference, which likely does not represent ground elevations as required. Third, the authors incorrectly named and interpreted the SRTM minus reference elevations by describing them as the “absolute error of the SRTM DEM. ” These shortcomings of the Zhou et al. paper warrant corrections to their findings.

Sustainable development of combined electricity and heat system (CEHS) can effectively facilitate the energy transition from fossil energy consumption to comprehensive utilization of multiple energy. The coupled energy system, featured with the risk of components failure and the integrated uncertain renewable energy sources (RESs), can face a safety and reliability operation challenge. Therefore, achieving the tradeoff between the economy and risk aversion for the CEHS operation is an urgent issue to be settled. This paper proposes a contingencies-based distributionally robust co-risk operation model (DRCROM) for the CEHS, which provides the failure risk sample of components applied by the non-sequenced Monte Carlo simulation (MCS) method and the penalty of energy spillage (including wind spilling and load shedding) under contingencies. Moreover, the uncertain power output risk of RESs can be addressed by using the distributionally robust individual chance-constrained (DRICC) methodology with Wasserstein metric ambiguity set. As a result, incorporating the uncertain component state and power output of RESs into the risk operation can be considered in this paper. Case studies can be conducted to give risk analyses that the average violation probability $V_{p}$ and the total co-risk dispatch cost $C^{\text {Obj}}$ are mainly affected by $\varepsilon $ . Contingencies can also increase risk indices and influence the dispatch energy and energy spillage.

Traceability information can be crucial for software maintenance, testing, automatic program repair, and various other software engineering tasks. Customarily, a vast amount of test code is created for systems to maintain and improve software quality. Today’s test systems may contain tens of thousands of tests. Finding the parts of code tested by each test case is usually a difficult and time-consuming task without the help of the authors of the tests or at least clear naming conventions. Recent test-to-code traceability research has employed various approaches but textual methods as standalone techniques were investigated only marginally. The naming convention approach is a well-regarded method among developers. Besides their often only voluntary use, however, one of its main weaknesses is that it can only identify one-to-one links. With the use of more versatile text-based methods, candidates could be ranked by similarity, thus producing a number of possible connections. Textual methods also have their disadvantages, even machine learning techniques can only provide semantically connected links from the text itself, these can be refined with the incorporation of structural information. In this paper, we investigate the applicability of three text-based methods both as a standalone traceability link recovery technique and regarding their combination possibilities with each other and with naming conventions. The paper presents an extensive evaluation of these techniques using several source code representations and meta-parameter settings on eight real, medium-sized software systems with a combined size of over 1.25 million lines of code. Our results suggest that with suitable settings, text-based approaches can be used for test-to-code traceability purposes, even where naming conventions were not followed.

To improve the operation performance and energy conversion efficiency of the redox flow battery (RFB), a modular active balancing circuit for redox flow battery applied in the energy storage system is proposed in this paper. Detailed topology description, parameter design, characteristic analysis, operation principle and control strategy of the proposed circuit are presented and discussed in the paper. Different from the conventional battery balancing circuit, the key point of the proposed balancing circuit is that it integrates the circulating pump driving circuit and the state-of-charge (SOC) equalization circuit of the redox flow battery. It uses the energy consumption of the pump driving to balance the SOCs of different battery stacks, which provides a new SOC balancing mechanism for RFB. Based on the proposed circuit, an active balancing control strategy using the time-sharing energy transmission method is proposed, in which the sub-modules of the circuit are alternatively configured in charge-state and discharge-state for absorbing energy or releasing energy to achieve the SOC balancing control for RFB. Compared with the conventional balancing solution or circuit, the proposed modular active balancing circuit simplifies the complexity of the battery management system for RFB, which has the advantages of high efficiency, high reliability, and good scalability. Simulations on Matlab/Simulink and experiments on a downscaled prototype were carried out to verify the feasibility and effectiveness of the proposed circuit and control strategy.

Terrain generation aims to automatize the procedure of creating landscapes using a computer system. The generation models must follow different terrains’ topographical features, such as areas with river deltas and other regions where water bodies affect the natural landscapes. It is possible to generate more realistic terrains thanks to improvements in computer graphics techniques and deep learning models that use specific hardware. However, the advance on the generation of terrains that include river deltas, fjords, and waterfalls has not had the same pace as other more studied landscapes. Therefore, as a contribution to the advance of the research of terrain generation with water bodies using generative models, this paper presents the DRCA2020 dataset, which is useful for supervised training. The proposed dataset contains eight different types of real-world satellite images. These images are grouped by the same geographical location. There are 13,184 groups; each one has three RGB surface images, a water coverage map, three binarizations of water coverage, and a digital elevation model (DEM). Additionally, this paper proposes the use of a cGAN composite model, trained with the DRCA2020 dataset, for generating synthetic DEMs from water coverage images and therefore, to create realistic surface texture images with promising validation results.

Precision Agriculture (PA) is a management strategy that utilizes communication and information technology for farm management. It is a key to improve productivity by using the best agricultural practices and optimal usage of resources. Agriculture faces diverse challenges due to soil degradation, climate variation, and increasing costs. To unfold these challenges, PA uses Wireless Sensor Networks(WSNs) and exploits acquisition, communication, and processing of the data as basic enabling technologies to amplify the crop yield. Also, many other multidisciplinary technologies are supporting PA in finding the most novel use cases for PA. The use of Machine Learning (ML) and Artificial Intelligence (AI) has transformed PA at almost every level. The fog/edge paradigm is mitigating many challenges such as network bandwidth and security by bringing computation closer to the deployed network. At the same time, Software Defined Networks (SDN) brings flexibility, big data assists in handling data, and nanotechnology plays a crucial part in driving the innovation in PA. This paper delves into ways these technologies are transforming PA in respective tracks, exhibiting the significance of integrating multidisciplinary approaches towards the future of PA. In addition to a comprehensive survey, this paper proposes a multidisciplinary architecture: AgriFusion, for efficient and cost-effective agriculture solutions. A list of industrial solutions for different aspects of farm management and their underlying focused technology have been highlighted. This can help to align research and industrial goals for PA. Furthermore, this paper defines a step approach to describe the performance dichotomy between resource availability and objectives for PA. In addition, solution architecture is proposed for designing Key Performance Indicators (KPI) in PA. In the end, some open research issues in implementing PA and respective future scopes have been presented.

DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes. Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems. Nonetheless, few methods have been proposed to address the intersection of multi-class imbalanced and high-dimensional problems concurrently due to their intricate interactions. This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization algorithm (GOA). The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm. The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA. The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.

In the current competitive world, producing quality products has become a prominent factor to succeed in business. In this respect, defining and following the software product quality metrics (SPQM) to detect the current quality situation and continuous improvement of systems have gained tremendous importance. Therefore, it is necessary to review the present studies in this area to allow for the analysis of the situation at hand, as well as to enable us to make predictions regarding the future research areas. The present research aims to analyze the active research areas and trends on this topic appearing in the literature during the last decade. A Systematic Mapping (SM) study was carried out on 70 articles and conference papers published between 2009 and 2019 on SPQM as indicated in their titles and abstract. The result is presented through graphics, explanations, and the mind mapping method. The outputs include the trend map between the years 2009 and 2019, knowledge about this area and measurement tools, issues determined to be open to development in this area, and conformity between conference papers, articles and internationally valid quality models. This study may serve as a foundation for future studies that aim to contribute to the development in this crucial field. Future SM studies might focus on this subject for measuring the quality of network performance and new technologies such as Artificial Intelligence (AI), Internet of things (IoT), Cloud of Things (CoT), Machine Learning, and Robotics.

Energy plays a pivotal role for economic development of a country. A reliable energy source is needed to improve the living standards of people. To achieve such a goal, governments and industries are trying to install a new energy infrastructure called the “Smart Grid”. This helps to manage the electricity generation and distribution in an efficient manner. Buildings and other structures are the biggest consumers of electricity. There is a need to reduce the energy consumption so that the resources can be utilized efficiently. Therefore, in this paper, we give a comprehensive state-of-the-art on various recent techniques and solutions which provide energy savings in smart homes and buildings. This includes statistical models, cloud computing based solutions, fog computing and smart metering based architectures, and several other IoT (internet of things) inspired solutions. We also present a hypothetical model that treats energy supply and usage in buildings as a self-managing energy system (SES). This paper is concluded by highlighting several open issues and challenges related to energy management in buildings

Funding Information: This work was supported by FinElib, Finland, through the FinELib consortium's agreement with IEEE. Publisher Copyright: © 2013 IEEE. In this paper, a new family of ultra-high step-up DC-DC converters based on a center-tapped coupled inductor (CI) is proposed. These single-switch converters employ different inductive and capacitive power transfer techniques by utilizing multi-winding CIs, intermediate capacitor links and simple switched-capacitors to improve the transferred power rate, harvest magnetizing and leakage inductance energies, and enhance power density. Achieving high voltage gain in low duty cycle values enables the proposed converters to operate under wide output voltage ranges; meanwhile, distributing the output voltage on two or three output ports alleviates the voltage stress on output terminal components. Low input current ripple, simple pulse width modulation control, low switch voltage stress and operation without circulating current can be listed as other features. In this paper, the proposed family is introduced, theoretically analyzed and compared with other state-of-the-art researches. Finally, the accuracy of analyses are evaluated with some experimental tests of a 1.25 kW experimental prototype.

In recent times, numerous convolutional neural network (CNN) based detection models have been proposed and have shown excellent performance. However, because these models are generally developed to detect objects in class units (e.g., person, car), additional training processes with numerous datasets are required to find a specific object. This paper proposes a model that accurately detects specific persons by using top clothing color information without any additional training processes. The proposed method combines CNN-based instance segmentation and pose estimation, utilizing all the advantages of each technique. To avoid redundant computations, these two schemes are implemented as a filtering-based sequential operation structure. As a result, the proposed method has a 92.57% of accuracy in detecting a specific person with only a slight processing speed decrease. Furthermore, in this paper, the proposed model is efficiently ported on the heterogeneous embedded platform (i.e., NVIDIA Jetson AGX Xavier) with a parallel processing technique to maximize the hardware utilization.

Communication between the nodes in a vehicle is performed using many protocols. The most common of these is known as the Controller Area Network (CAN). The functionality of the CAN protocol is based on sending messages from one node to all others throughout a bus. Messages are sent without either source or destination addresses. Consequently, it is simple for an attacker to inject malicious messages. This may lead to some nodes malfunctioning or total system failure, which can affect the safety of the driver as well as the vehicle. Detecting intrusions is a challenging problem in the context of using CAN bus for in-vehicle communication. Most existing work focuses on the physical aspects without taking into consideration the data itself. Machine Learning (ML) tools, especially classification techniques, have been widely used to address similar problems. In this paper, we use and compare several ML techniques to deal with the problem of detecting intrusions in in-vehicle communication. An experimental study is performed using a real dataset extracted from a KIA Soul car. Compared to previous work, which focuses on detecting intrusions based on the physical aspect, this paper aims to concentrate on the application of data analysis and statistical learning techniques. Furthermore, the paper provides a comparative study of the most common ML techniques. The results show that the techniques under consideration in this paper outperform other techniques that have been used previously.

A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. These affiliated relationships exhibit a series of valuable human-centered organizational knowledge and are utilized for exploring human resource’s work patterns. This paper focuses not only on a specific type of affiliated relationships between performers and activities, in particular, which forms a performer-to-activity affiliation network, but also on a specific type of analysis techniques, which builds a closeness centrality measurement approach for quantifying the degrees of farnesses between performers as well as between activities. In other words, this paper investigates a series of formal approaches for building organizational closeness centrality measurement techniques on the specific type of affiliation networks. The investigation mainly deploys two types of algorithmic formalisms along with an operational example, which are measuring performer-centered organizational closeness centralities and activity-centered organizational closeness centralities, respectively. In order to validate the deployed algorithmic equations, the paper carries out a couple of operational experiments; One is on an ICN-based workflow package and the other is on a discovered workflow model mined from a dataset of workflow event logs. Summarily, this paper devises a series of algorithms and equations for measuring closeness centralities of activities, verify the devised algorithms and their related equations along with operational examples, and discuss the ultimate implications of these analysis techniques of organizational closeness centrality measurements as the performer-to-activity affiliation networking knowledge in workflow-supported organizations.

Neuromorphic vision sensors provide low power sensing and capture salient spatial-temporal events. The majority of the existing neuromorphic sensing work focus on object detection. However, since they only record the events, they provide an efficient signal domain for privacy aware surveillance tasks. This paper explores how the neuromorphic vision sensor data streams can be analysed for human action recognition, which is a challenging application. The proposed method is based on handcrafted features. It consists of a pre-processing step for removing the noisy events followed by the extraction of handcrafted local and global feature vectors corresponding to the underlying human action. The local features are extracted considering a set of high-order descriptive statistics from the spatio-temporal events in a time window slice, while the global features are extracted by considering the frequencies of occurrences of the temporal event sequences. Then, low complexity classifiers, such as, support vector machines (SVM) and K-Nearest Neighbours (KNNs), are trained using these feature vectors. The proposed method evaluation uses three groups of datasets: Emulator-based, re-recording-based and native NVS-based. The proposed method has outperformed the existing methods in terms of human action recognition accuracy rates by 0.54%, 19.3%, and 25.61% for E-KTH, E-UCF11 and E-HMDB51 datasets, respectively. This paper also reports results for three further datasets: E-UCF50, R-UCF50, and N-Actions, which are reported for the first time for human action recognition on neuromorphic vision sensor domain.

With the increased number and reduced cost of smart devices, Internet of Things (IoT) applications such as smart home (SHome) are increasingly popular. Owing to the characteristics of IoT environments such as resource constrained devices, existing authentication solutions may not be suitable to secure these environments. As a result, a number of authentication solutions specifically designed for IoT environments have been proposed. This paper provides a critical analysis of existing authentication solutions. The major contributions of the paper are as follows. First, it presents a generic model derived from an SHome use-case scenario. Secondly, based on the model, it performs a threat analysis to identify possible means of attacks. The analysis leads to the specification of a set of desirable security requirements for the design of authentication solutions for SHome. Thirdly, based on the requirements, existing authentication solutions are analysed and some ideas for achieving effective and efficient authentication in IoT environments are proposed.

The emergence of fast-switching wide-bandgap (GaN, SiC) power electronics devices has enabled motor drive systems to achieve high efficiency, power density, control bandwidth and high-level of integration. However, the fast-switching speed and high switching frequency result in an increased level of motor overvoltage at both motor terminals and stator neutral. This overvoltage increases the stress across the motor winding insulation and bearings. This paper investigates three types of motor overvoltages, i.e., differential mode (DM) (phase-to-phase) motor terminal overvoltage, common mode (CM) (phase-to-ground) motor terminal overvoltage and CM stator neutral overvoltage (motor neutral to ground). Both the high switching speed (high dv/dt) effect and the high switching frequency effect on the motor overvoltage are investigated in this paper, where the high switching frequency effect has not been fully addressed in existing literature. Significant overvoltage is observed when the switching frequency or its multiples coincide with the cable or motor anti-resonant frequency. The anti-resonant behavior in the cable and motor impedance has been used to identify the overvoltage oscillation frequency and to explain the overvoltage observations for the three types of motor overvoltages. The analysis has been tested using a 7.5kW motor setup with and without a four-core cable, driven by a SiC or a GaN three-phase inverter with a switching frequency up to 250kHz and switching speed up to 40kV/ $\mu \text{s}$ . In addition, the motor bearing current, which is a main cause of bearing degradation, has also been tested and the increase of bearing current is observed due to the high frequency effect.

The journey towards transportation electrification started with small electric vehicles (i.e., electric cars), which have enjoyed an increasing level of global interest in recent years. Electrification of commercial vehicles (e.g., trucks) seems to be a natural progression of this journey, and many commercial vehicle manufacturers have shifted their focus on medium- and heavy-duty vehicle electrification over the last few years. In this paper, we present a comprehensive review and analysis of the existing works presented in the literature on commercial vehicle charging. The paper starts with a brief discussion on the significance of commercial vehicle electrification, especially heavy- and medium-duty vehicles. The paper then reviews two major charging strategies for commercial vehicles, namely the return-to-base model and the on route charging model. Research challenges related to the return-to-base model are then analysed in detail. Next, different methods to charge commercial vehicles on route during their driving cycles are summarized. The paper then analyzes the challenging issues related to charging commercial vehicles at public charging stations. Future works relevant to these challenges are highlighted. Finally, the possibility of accommodating vehicle to grid technology for commercial vehicles is discussed.

Context: Quality Control (QC) has been constantly an essential concern in many fields like food industry production, medical drugs, environmental protection, and so on. An odor or flavor, as a global fingerprint, can be implemented as a non-invasive mechanism for quality assurance. This computer-based approach can assure accurate detection and precise identification of the product quality or manufactured goods. Objective: This paper aims to achieve a systematic review about e-nose by introducing the achievements made by researchers in this area, to summarize their findings, to provide motivations and challenges to new researchers in the field of e-nose. Methods: The articles that were being utilized in the e-nose field were systematically achieved using three search engines: The online library of IEEE Explore, Web of Science and Science Direct for time span of 7 years (from 2013 to 2020). Both medical literature reviews and technical reviews were considered in the criteria of the research for wider understanding in the field of e-nose. The articles were categorized according to the objective of the research and projected into four classes. Upon completion of screening process 333 research papers using the exclusion and inclusion conditions, as the final set 54 articles were selected. Results: The taxonomy of this research was classified into four categories. The first one included the suggested methods that introduced the utilization of the e-nose for classification purposes (9/54 papers). The second category comprises the methods related to the development of e-nose (24/54 papers). The third one included the review studies about the e-nose (8/54 papers). The fourth group comprises comparative studies and evaluation (13/54 papers). Discussion: This systematic review contributes for a clearer understanding and a full insight in the e- nose research field by surveying and categorizing pertinent research efforts. Conclusion: This review paper will help to address the up-to-date research opportunities, challenges, problems, motivations and recommendations related to the utilization of e-nose in all fields of sciences and industries.

This paper proposes a two-stage stochastic energy management (EM) in an isolated microgrid (MG) to decide for the day-ahead optimal dispatch. The dispatch aims to effectively manage the MG power sources, including intermittent renewable energy sources (RESs), battery energy storage systems (BESSs), and diesel generators such that the expected operation costs, reactive power costs, spinning reserve, and load shedding are minimized. The Generative Adversarial Networks (GANs) is utilized in this paper as a data driven scenario generation method to model the uncertainties in the output power of the RESs to be used in the stochastic programming formulation. Then, the fast forward scenario reduction algorithm is used to reduce the number of scenarios with the help of SCENRED/GAMS software. Usually, fuel consumption costs of diesel generators are considered to be dependent on active power generation only. However, neglecting the related reactive power costs might result in increased operation costs and deviations in the dispatches from the optimal solutions. Hence, this paper co-optimizes the costs related to both active and reactive powers of diesel generators. In addition, this study considers the reactive power capability of inverter-interfaced distributed energy resources (DERs). Moreover, the detailed models for the different resources are presented, especially for diesel generators where the actual capability curves are used instead of the widely used box constraints. The problem is formulated as a nonlinear programming problem in the General Algebraic Modeling System (GAMS) software and is solved by the CONOPT solver.

Nowadays, one of the key areas of research on contact centre systems is their automation. The main element that influences the possibility of automation of contact centre processes is the call transcription methods implemented by automatic speech recognition (ASR) systems. Such systems enable developing intention recognition methods and, consequently voice bots. The current solutions used in ASR systems for many less popular languages do not guarantee a fully satisfactory transcription quality for hotline voice calls. This is due to the unique characteristics of the sound signal generated there, whose quality parameters differ significantly from those of studio recordings. The paper presents a comparative study of selected speech recognition systems that were additionally supplemented with elements of preprocessing of sound recordings and postprocessing of originally produced transcriptions. As for preprocessing, the following methods were tested: separation of the client and agent channels into two independent signals, training of ASR systems, and audio signal correction. With regards to postprocessing, on the other hand, tests were performed for inarticulate sounds, normalization of standard phrases (e.g. numbers, dates, times, etc.), and identification of close-sounding phrases and foreign language phrases, and lemmatization. Based on the research conducted and the analyses performed, a new method of call transcription intended specifically for contact center systems was proposed. The research conducted for this paper was based on the Polish language model, for which major problems are observed with the quality of automatic contact center call transcriptions.

Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.

In a smart urban environment, providing accurate information about human activities is an important task. It is a trend to implement human activity recognition (HAR) algorithms and applications on smart phones, including health monitoring, self-management system, health tracking and so on. However, human activity recognition(HAR) is very complex, and it is important to use the best technology and machine learning to understand human activities. One of the main problems of the existing HAR strategies is that the classification accuracy is relatively low, and in order to improve the accuracy, it needs high computational overhead. The purpose of this paper is to use an exploratory data analysis method to deal with HAR, after the implementation of different data mining techniques, the results of dimensionality reduction and visualization are obtained. The HAR method based on smart phone and EDA proposed in this paper is a high precision method. Compared with other classifiers, its accuracy is 96.56%. This article will discuss computational prediction activities and the computational limitations of using exploratory data to analyze (EDA) on 564 feature data frames. The experimental results show that HAR-based exploratory data analysis is a common sensory signal processing technology. The GridSearchCV and LinearSVC algorithm can provide accurate automatic human activity recognition (HAR), for elderly and disabled patients in need of continuous care, and it is a decision-making tool to support sports coaches to make plans.

Considering smartphone-based indoor localization and tracking, combination of Wi-Fi fingerprinting and pedestrian dead reckoning (PDR) becomes prevalent due to wide deployment of indoor Wi-Fi access points and easily access of inertial measurement units (IMUs) on smartphones. Since Wi-Fi fingerprinting depends on the similarities between online and offline received signal strengths, it suffers from the fluctuation of radio signal measurements. Meanwhile, the PDR relies on the IMUs, which undergo accumulated errors in long walking distance. In this paper, we aim to improve the indoor tracking accuracy, while filling up the gap between two popular Android and iOS platforms. In particular, we propose a hybrid tracking algorithm that integrates the PDR with Wi-Fi/iBeacon signal features. There are three key points in our paper. First, we process Wi-Fi/iBeacon signals to build conversion functions to convert signals to related proximity distances. Besides, in order to support mobile tracking, we introduce a placement methodology for iBeacon installation for a specific region of interest. Second, a mobile smartphone uses an improved PDR to localize itself, while incorporating Wi-Fi/iBeacon data for estimating a starting point and correcting its location along the walking path. Finally, we build an iOS app to implement the proposed scheme and display visual tracking results. Experiment results show that our proposed scheme is more robust and accurate compared to the conventional schemes.

The timetabling problem is common to academic institutions such as schools, colleges or universities. It is a very hard combinatorial optimisation problem which attracts the interest of many researchers. The university course timetabling problem (UCTTP) is difficult to address due to the size of the problem and several challenging hard and soft constraints. Over the years, various methodologies were proposed to solve UCTTP. The purpose of this survey paper is to provide the most recent scientific review of the methodologies applied to UCTTP. The paper unveils a classification of methodologies proposed in recent years based on chronology and datasets used. Perspectives, trends, challenges and opportunities in UCTTP are also presented. It is observed that meta-heuristic approaches are popular among researchers. This is followed closely by hybrid methodologies. Hyper-heuristic approaches are also able to produce effective results. Another observation is that the state-of-art methodologies in the scientific literature are not fully utilised in a real-world environment perhaps due to the limited flexibility of these methodologies.

The importance of organizational resilience as an effective means to cope with crises and achieve sustainable growth in crisis situations is gradually gaining attention from both theoretical and practical circles. Based on the review of existing research on organizational resilience, this study uses the scientific knowledge mapping method from the macro perspective and the literature analysis method from the micro level to review the development of organizational resilience research during 1984-2021, and analyzes 108 representative papers on organizational resilience. Finally, this study proposes future research directions based on the existing studies, such as enriching the influence factors of organizational resilience, strengthening the study of the mechanism of organizational resilience and strengthening the study of the effect of organizational resilience. This paper not only provides a reference for domestic scholars to construct an overall framework for organizational resilience research, but also provides theoretical and practical guidelines for enterprises to achieve rebound survival and then sustainable growth in the face of adversity.

The increasing demand for the development and utilization of renewable energy resources has lead to a growing interest in the development of LVDC systems. The dc grid provides more flexibility in integrating different forms of renewable energy sources effectively. However, the lack of a reliable protection mechanism remains the main drawback in the growth of dc distribution systems. Unlike ac systems, the absence of a natural zero crossing in dc systems for arc extinction makes the use of a conventional mechanical circuit breaker (MCB) a less reliable solution. The use of a solid-state circuit breaker (SSCB) results in fast fault interruption but reduces the overall efficiency of the system due to the on-state voltage drop of the semiconductor devices. A hybrid circuit breaker (HCB) combining MCB and SSCB yields better static and dynamic performances but the main challenges remains in the demagnetization of the transmission line inductance after a fault interruption and an arc formation between the MCB contacts. This paper proposes a hybrid circuit breaker (HCB) which is suitable for fast fault interruption in low voltage dc (LVDC) systems while alleviating the above issues. Proposed topology employs a semiconductor switch as well as an actively switched capacitor branch in parallel with the main mechanical breaker to facilitate fast current commutation during a fault. The mechanical breaker forming the main branch is turned off at zero voltage. This eliminates the arc formation across the moving contacts of the breaker. Moreover, the fault interruption process does not require a varistor for network demagnetization following the fault current commutation. This paper also presents a discharging mechanism for the capacitor in a practical implementation. Operation of the proposed dc circuit breaker is evaluated through a prototype tested in the laboratory.

Artificial intelligence-assisted diagnosis systems are developing rapidly, but doctors are currently less aware of artificial intelligence-assisted diagnosis systems. Understanding how to allow doctors to accept and use artificial intelligence medical assistant diagnosis system can promote the implementation of artificial intelligence medical assistant diagnosis system applications. Taking into account the current difficulties faced by the Internet of Things medical consultation services, this paper proposes a business operation model based on multi-party participation and sharing of medical consultation resources. We designed the information flow, overall logic and service implementation process of the service model, and completed the construction of the artificial intelligence medical service service model. We combine IoT technology to build a vital signs monitoring environment and clarify how to use IoT devices. In the error backpropagation algorithm, there is no significant difference in the contribution of different samples to the weight change, which makes the adjustment of network parameters not easily affected by difficult medical consultation samples, thereby weakening the effect of network medical consultation. In order to solve this problem, this article defines the degree to which the sample belongs to its correct category as the confidence of medical inquiry, and divides the training sample into a dangerous sample and a safe sample according to a dynamic threshold. Based on the difficulty of medical inquiry, an improved new learning algorithm is proposed. The algorithm penalizes the loss of dangerous samples, so that the convolutional neural network pays more attention to dangerous samples and can learn more effective information. Aiming at the eight physiological characteristics of data, this paper adjusts the structure of the convolutional neural network to make it take into account the richness of data characteristics and the dynamics of data changes over time. The realized CNN optimization algorithm model has improved the prediction effect, and the accuracy rate of medical consultation reaches 90.15%, which is better than other machine learning algorithms.

Single-phase fault is the most commonly occurred and severe AC fault that causes most of the Commutation Failures (CF) in LCC HVDC systems. It has long been regarded as having the same level of adverse impact on commutations of both 6-pulse bridges in the 12-pulse LCC HVDC scheme. This has resulted in the same level of investment on both 6-pulse bridges, for CF mitigation/elimination, e.g. firing angle advancement and equipment investment. However, due to different winding configurations of converter transformers, single-phase fault actually affects each 6-pulse bridge to different degrees. Based on the previously proposed AC Filterless Flexible LCC HVDC, this paper shows that, compared with the Y-Y bridge, the Y- $\Delta $ bridge has much smaller undesired phase advancement of the actual commutation voltage (commutation voltage at secondary side of converter transformer) under severe single-phase fault. As a result, the required voltage rating of controllable capacitors for Y- $\Delta $ bridge can be reduced, leading to considerable savings from reduced equipment costs and lower losses of controllable capacitors without compromising the commutation performance. The detailed theoretical analysis in this paper provides a valuable foundation for the development of more efficient CF mitigation/elimination strategies. Simulation results of 1) CIGRE HVDC benchmark system and 2) the same benchmark system but connected to a 39-bus inverter AC network are presented to validate the correctness of the analysis.