Plain reinforcement learning (RL) may be prone to loss of convergence, constraint violation, unexpected performance, etc. Commonly, RL agents undergo extensive learning stages to achieve proper functionality. This is in contrast to classical control algorithms, which are typically model-based. A direction of research is the fusion of RL with such algorithms, especially model-predictive control (MPC)

The significant benefits of DC microgrids have instigated extensive efforts to be an alternative network as compared to conventional AC power networks. Although their deployment is ever-growing, multiple challenges still occurred for the protection of DC microgrids to efficiently design, control, and operate the system for the islanded mode and grid-tied mode. Therefore, there are extensive research

Blockchain technology (BC) offers an innovation platform for decentralized and transparent transactions in the maritime port industry. This technology allows guaranteeing trust, transparency and traceability of cargo and data to be tracked. Today, in the port systems of emerging market countries, BC technology is increasingly being incorporated into information and communication processes. In parallel

The penetration of electric vehicles (EVs) in the transportation sector is increasing but conventional internal combustion engine (ICE) based vehicles dominates. To accelerate the adoption of EVs and to achieve sustainable transportation, the bottlenecks need to be elevated that mainly include the high cost EVs, range anxiety, lack of EV charging infrastructure, and the pollution of the grid due to

Preserving an appropriate population diversity is critical for the performance of evolutionary algorithms. In this paper, we present a co-evolutionary niching strategy (CoEN) to dynamically evolve appropriate niching methods and incorporate it into differential evolution (DE) to preserve the population diversity. The proposed CoEN strategy is achieved by optimizing a criterion, which involves both

In this paper, we present an application-specific Multi-Access Edge Computing (MEC) network architecture by leveraging the Control and User Plane Separation (CUPS) in mobile core networks to offload data processing from central servers to edge servers to reduce the transmitted traffic volume and also the response latency of connected vehicle mobility service. We first apply deep learning to classify

This paper describes FPGA implementation of a high-order continuous-time multi-stage noise-shaping (MASH) $\Delta \Sigma $ time-to-digital converter (TDC). The TDC is based on Gated Switched-Ring Oscillator (GSRO) and employs multirating technique to achieve improved performance over conventional $\Delta \Sigma $ TDCs. The proposed TDC has been implemented on an Altera Stratix IV FPGA development board

It is demonstrated that in this paper a peanut shaped structure cascaded with up-taper fiber structure can realize the inter mode interference between the Erbium doped fiber (EDF) core mode and cladding modes in fiber ring laser (FRL). A simple and inexpensive Mach–Zehnder interferometer (MZI) based on this structure is proposed. Shown from experimental results, the optical intensity of the core mode

In this paper we propose LoRaSyNc (LoRa receiver with SyNchronization and Cancellation), a second generation LoRa receiver that implements Successive Interference Cancellation (SIC) and time synchronization to improve the performance of LoRa gateways. Indeed, the chirp spread spectrum modulation employed in LoRa experiences very high capture probability, and cancelling the strongest signal in case

This paper explores the magnetic flux emissions of induction heating systems compared with inductively coupled heating systems. Inductively coupled heating uses a resonator coil attached to the ferromagnetic load in order to improve energy transfer from the appliance to the load. The magnetic flux emissions of both kinds of systems are simulated, and their dependence on coil current and turn number

Palm oil sector is considered one of main economical contributions in countries such a Malaysia where approximately 6.1% of their Gross Domestic Product (GDP) is contributed. Low Oil Extraction Rate (OER) from poor quality fresh fruit bunches have led to a decrease the oil palm production. The traditional method of using human naked eye to inspect the Fresh Fruit Bunches (FFB) during reception process

Developer forums are essential for software engineers to solve their problems with the assistance of experts on such forums. However, sometimes the solutions (answers) of a problem are not satisfactory or challenging to select the potential answer. Information seekers usually browse all the answers within the question thread to get the potential answer. The manual selection of correct answers is a

Grid-forming power converters (GFMC) have been widely adopted in power systems as an attractive solution against the challenges imposed by the ever-increasing penetration of renewables. Despite its versatility, GFMC is employed only to provide islanded operation, grid regulations, and synthetic inertia. To further extend the use of GFMC in enhancing power system stability, this paper proposes a multi-rotor

Model-mediated teleoperation (MMT) employs an environment model at the master side to compute feedback output to the master at a faster rate. This approach improves system stability in the presence of time delay. MMT, however, does not generally perform well if the employed model is not accurate. The model mismatch is unavoidable when the environment is unknown in advance or varies. This paper proposes

This study examines the high temperature deformation and constitutive modeling of flow stress for 310s stainless steel. To this end, hot tensile experiments were conducted under the temperatures of 700° and 800° at the strain rates of 0.0002/s, 0.002/s, and 0.02/s. Flow stress was modeled using the Arrhenius type constitutive equation and neural network approach. Specifically, Regime-Switched Neural

In this work, we present a numerical study of 1D and 2D closely spaced antenna arrays of microstrip dipole antennas covered by a metasurface in order to properly cloak and decouple the antenna arrays operating at neighboring frequencies. We show that the two strongly coupled arrays fed by a microstrip-to-balanced transmission-line transition are effectively decoupled in 1D and 2D array scenarios by

Feature selection plays an important role in pattern recognition and smart computing. The full set of typical testors constitutes a useful tool for solving feature selection problems, especially those problems in which the objects are described by both quantitative and qualitative features. However, finding the typical testors involves a high computational cost. That is why even the most efficient

Model Predictive Controller (MPC) is a capable technique for designing Path Tracking Controller (PTC) of Autonomous Vehicles (AVs). The performance of MPC can be significantly enhanced by adopting a high-fidelity and accurate vehicle model. This model should be capable of capturing the full dynamics of the vehicle, including nonlinearities and uncertainties, without imposing a high computational cost

This paper proposes a flexible methodology to improve the definition of each distinct trip carried out in a transport system, integrating the information provided by stop-level events from its automated vehicle location and fare collection systems, and scheduling subsystem information at the initial stop of planned trips. The data are structured; and then corrected and completed utilizing several criteria

Using the non-causal nature of a fractional-order singular (FOS) model, this paper deals with the modification of an estimation algorithm developed by Nosrati and Shafiee, and demonstrates how the derived estimation procedure can be adjusted by additional information related to the future dynamics. The procedure adopts the maximum likelihood (ML) method leading to a 3-block fractional singular Kalman

As high-density SRAMs must be designed to ensure a substantially small failure rate, the accurate yield estimation with practically acceptable runtime of circuit simulations is highly challenging. Here, a read access yield estimation method for high-density static random access memory (SRAM) is proposed. Instead of performing SPICE runs for the entire SRAM circuit, the proposed method partitions the

Automatic detection of threatening language is an important task, however, most of the existing studies focused on English as the target language, with limited work on low-resource languages. In this paper, we introduce and release a new dataset for threatening language detection in Urdu tweets to further research in this language. The proposed dataset contains 3,564 tweets manually annotated by human

Traffic congestion is one of the challenges that face urban cities’ planners. It affects the environment as it increases the emissions of CO2 and affects the logistics systems as it may increase the travel time of different vehicles. Scheduling traffic signals is one of the ways to solve this problem. In the urban traffic signal scheduling problem, it is desired to get the optimum schedule for each

The power consumption of buildings over the course of each minute, hour, day and season plays a major role in how this load influences the Electric Power System voltage and frequency, and vice versa. This consumption is based on the building’s load component types, efficiencies, and how they consume power and react to changes in real time. Due to this complexity, standard full-building load models

Short-term voltage stability of power systems is governed by load dynamics, especially the proportion of small induction motors prevalent in residential air-conditioners. It is essential to efficiently monitor short-term voltage stability in real-time by detailed data analytics on voltage measurements acquired from phasor measurement units (PMUs). It is likewise critical to identify the location of

Traffic accidents on highways are a leading cause of death despite the development of traffic safety measures. The burden of casualties and damage caused by road accidents is very high for developing countries. Many factors are associated with traffic accidents, some of which are more significant than others in determining the severity of accidents. Data mining techniques can help in predicting influential

The joint shuffled scheduling decoding (JSSD) algorithm can reduce the decoding complexity of the joint source-channel coding system (JSCC) based on double protograph low-density parity-check (P-LDPC) codes. However, the JSSD algorithm will not work when the linking matrix between check nodes (CNs) of the source P-LDPC and variable nodes (VNs) of the channel P-LDPC is adopted in such a system, and

This article proposes a deep neural network (DNN) model to predict the electric field induced by a transcranial magnetic stimulation (TMS) coil under high-amplitude and low-frequency current pulse conditions. The DNN model is comprised of an input layer with 6 neurons, three non-linear hidden layers with a total of 1088 neurons, and a linear single output layer. The model is developed in Google Colaboratory

This paper introduces the a framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres which implements four design principles: Standardisation, Encapsulation, Abstraction, and Decoupling (SEAD). Although a large body of research has been formulating platooning manoeuvres, it is still challenging to design, describe, read, and understand them. This difficulty

In the present study, hydrofoil ribs made from NACA 2412 profile were mounted in a novel way at all walls of microchannel heat sink with various configurations to improve its thermo-hydraulic characteristics. The various configurations include comparison between staggered and aligned arrangements, and the variation of rib spacing between two consecutive hydrofoils (Sr = 0.2–0.6 mm) at a constant cord

The rapid expansion of Internet of Things (IoT) devices and applications has accelerated research in various areas of human development. However, the cost of commercial instrumentation impedes the momentum of technological growth in developing regions. In this study, a low-cost, low-power, wireless sensor network for groundwater monitoring (LWNGM) was developed to provide near real-time groundwater

The Photovoltaic (PV) cell converts a portion of incident irradiation of visible wavelength into electricity and the remaining into heat, thereby decreasing the electrical efficiency. Hence, it is necessary to transfer the heat generated in the module by a cooling medium, thereby maintaining the operating temperature within the operating limit. This study discusses the feasibility of cooling the monocrystalline

Social media network is one of the important parts of human life based on the recent technologies and developments in terms of computer science area. This environment has become a famous platform for sharing information and news on any topics and daily reports, which is the main era for collecting data and data transmission. There are various advantages of this environment, but in another point of

This paper reports on the design and practical validation of monopole antenna arrays with reduced half-power beamwidth (HPBW) variation in a bandwidth (BW) greater than one octave of the RF spectrum. The HPBW variation is reduced by feeding the antenna elements with frequency-dependent feed networks. The feed networks are designed in such a way that the element power is gradually shifted from the outer

We report the development of a novel soft body touch sensor capable of measuring contact force along its length. The sensor can be embedded in soft objects and on curved surfaces for smart furniture, interactive electronics, and general robotics applications. The sensor uses pneumatic transduction principle and achieves both high sensitivity and impact loading tolerance. The development is motivated

Semi-supervised domain adaptation (SSDA) is a promising technique for various applications. It can transfer knowledge learned from a source domain having high-density labeled samples to a target domain having limited labeled samples. Several previous works have attempted to reduce the distribution discrepancy between source domain and target domain by using adversarial-based or entropy-based methods

Large-scale photovoltaic (PV) power plant has witnessed a dramatic increase in the integration into transmission and distribution network, manifesting subsynchronous control interaction (SSCI) when the host grid is weak. In this work, the oscillation modes of a typical PV network are analyzed, and a faster-than-real-time (FTRT) emulation is proposed for predicting the SSCI and consequently mitigating

Social media plays a crucial role in shaping the worldview during election campaigns. Social media has been used as a medium for political campaigns and a tool for organizing protests; some of which have been peaceful, while others have led to riots. Previous research indicates that understanding user behaviour, particularly in terms of sentiments expressed during elections can give an indication of

Authorship attribution is the study to identify individuals by their writing styles without knowing their actual identities. This is a challenging task in natural language processing. Most work on authorship attribution focused on English, whereas, the problem is understudied in Arabic language. However, due to the complex and distinct morphological nature of the Arabic language, techniques developed

Electricity theft has emerged as one of the major reasons of Non-Technical Losses (NTLs) in the power distribution systems and has become a global issue. Therefore, power utilities are concerned about resolving the issue of electricity theft. In this regard, the data collected by Advanced Metering Infrastructure (AMI) can be used to devise data-driven machine learning-based Electricity Theft Detection

Incremental learning requires a learning model to learn new tasks without forgetting the learned tasks continuously. However, when a deep learning model learns new tasks, it will catastrophically forget tasks it has learned before. Researchers have proposed methods to alleviate catastrophic forgetting; these methods only consider extracting features related to tasks learned before, suppression to extract

In this paper, a distributed adaptive consensus law with fault compensation for a heterogeneous multi-robot system (MRS) is proposed. The design paradigm adopted in this work involves a leader-following cooperative algorithm featuring two distinct adaptive coupling gains to compensate for multiple additive time-varying faults. Exacerbating the situation, the follower robots commissioned in the leader-following

Image feature detection is a key task in computer vision. Scale Invariant Feature Transform (SIFT) is a prevalent and well known algorithm for robust feature detection. However, it is computationally demanding and software implementations are not applicable for real-time performance. In this paper, a versatile and pipelined hardware implementation is proposed, that is capable of computing keypoints

There is an increasing pressure to improve the cost-effectiveness of orthopaedic training, both temporally and financially. Accordingly, virtual reality (VR) has been incorporated into a number of surgical training programs, providing trainees a safe yet realistic environment to practice their craft before going into the operating room. Identification of critical success factors (CSFs) for VR integration

Data center communication networks are characterized by fast and intensive traffic changes, to the extent that a statistical approach to routing has significant drawbacks. This paper proposes the first routing solution that takes into account every data center flow. In order to achieve the required performance, the proposed algorithm operates in the data plane, i.e. in the packet processing pipeline

The fitness-dependent optimizer (FDO), a newly proposed swarm intelligent algorithm, is focused on the reproductive mechanism of bee swarming and collective decision-making. To optimize the performance, FDO calculates velocity (pace) differently. FDO calculates weight using the fitness function values to update the search agent position during the exploration and exploitation phases. However, the FDO

In the successive cancellation list decoding of polar codes, the metric sorting dominates the overall decoding latency. To reduce the latency of metric sorting, this paper proposes a new sorting method, called interleaved local sorting, which divides the metrics to be sorted into several groups and locally sorts each group independently. In addition, an interleaving scheme is proposed to recover the

Establishing a session key (SSK) is very important for real-world deployment in open networks, which enables secure communication between remote parties. In the past, some authenticated key exchange (AKE) protocols have been proposed to generate a SSK, but the certificate management issue is inhered in the traditional public key infrastructure and must be addressed. To tackle this issue, the identity

The International Union of Railway provides an annually safety report highlighting that human factor is one of the main causes of railway accidents every year. Consequently, the study of human reliability is fundamental, and it must be included within a complete reliability assessment for every railway-related system. However, currently RARA (Railway Action Reliability Assessment) is the only approach

For dealing with the electricity theft detection in the smart grids, this article introduces a hybrid deep learning model. The model tackles various issues such as class imbalance problem, curse of dimensionality and low theft detection rate of the existing models. The model integrates the benefits of both GoogLeNet and gated recurrent unit (GRU). The one dimensional electricity consumption (EC) data

In the field of underwater acoustic signal processing, the ship radiated noise contains a large amount of ship information, which is of great significance to the ship identification. The traditional method relies too much on the operator and prior knowledge, which seriously reduces the efficiency and accuracy of the ship radiated noise identification. This paper presented a novel ship radiated noise

Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor. This reduces the computational overhead and improves the correctness of the classifier. Attribute Selection is commonly carried out by applying some

The Manta ray foraging optimization (MRFO) is a novel swarm-based metaheuristic optimizer. It is mainly modeled by simulating three foraging behaviors of the Manta rays, which has a good performance. However, several drawbacks of MRFO have been noticed by analyzing its mathematical model. Random selection of reference points in the early iterations weakens the exploitation capability of MRFO. Chain

Spatial audio has attracted more and more attention in the fields of virtual reality (VR), blind navigation and so on. The individualized head-related transfer functions (HRTFs) play an important role in generating spatial audio with accurate localization perception. Existing methods only focus on one database, and do not fully utilize the information from multiple databases. In light of this, a pre-trained-based

An automatic train protection (ATP) system is the core to ensure operation safety of high-speed railway. At present, failure rate change rules of the system are not well understood and the maintenance strategy is not refined. In order to improve the protection capability and maintenance level of high-speed trains, this paper proposes a decision tree machine learning model for failure feature extraction

Statistical modeling and analysis of satellite failure is an effective way to master the satellite operating status, and the results can provide important theoretical basis for the performance improvement of satellite reliability. In view of the dual-period characteristics of satellite failure distribution, a 2-Weibull segmented model for statistical modeling of satellite failure has been proposed

Wireless Sensor Networks (WSNs) are broadly applied for various applications in tracking and surveillance due to their ease of use and other distinctive characteristics compelled by real-time cooperation among the sensor nodes. In WSNs, security is becoming a critical issue, as the techniques for malicious node detection adopt a one-time, centralized decision-making approach. With this paradigm, errors

This paper proposes an improved back-stepping control approach and its application to small nonlinear UAV control systems with uncertainties such as external disturbance. Unlike traditional back-stepping control methods, the idea of prescribed performance function (PPF) is incorporated into the control design, such that both the transient and steady-state control performance can be strictly guaranteed

Cataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet , is proposed for automatic cataract detection in fundus

Microgrids are used to incorporate distributed generators (DGs) together so that DGs can be scheduled to satisfy the load demand. Usually droop controllers are adopted so that each DG shares the load equally. To improve the flexibility in system configuration with DGs of different capacities, droop controllers should be updated. In order to share the load among DGs in proportion to each unit’s capacity