Conventional Schemes Research Articles

A Scalable Deep-Learning-Based Active User Detection Approach for SEU-Assisted Cell-Free Massive MIMO Systems

Massive ultra-reliable and low-latency communications (mURLLC) is an emerging and dominate traffic service in 6G. To reduce the signaling overhead and access delay, grant-free random access (GFRA) is widely used in mURLLC. As the first step in GFRA, active user detection (AUD) is aimed to identify the set of active users accurately and timely. Conventional AUD schemes relying on iterative computations over massive users bring redundant computing overload and processing delay, which seriously affect the system scalability in the mURLLC scenario. Considering the near-real-time requirement of mURLLC, we propose a scalable deep learning-based AUD approach utilizing similar channel sparsity in cell-free (CF) massive multiple input multiple-output (mMIMO) systems. By exploiting the distributed computing unit, i.e., space expansion unit (SEU), we design a SEU-assisted CF mMIMO to improve the scalability of the traditional centralized CF computing architecture. In the proposed system, all access points (APs) are divided into several clusters, and the SEU in each cluster provides a reliable distributed AUD scheme through a one-dimensional convolutional network (1D CNN). In addition, a transfer learning-based ensemble model is established at the CPU to achieve a better global detection decision. Simulation results demonstrate the superiority of our scalable deep learning-based approach, and reveal that through the transfer learning-based model fusion at the CPU, our proposed scalable SEU-assisted approach can obtain success probability close to that of the centralized CF computing scheme with less access delay. In addition, our scheme requires fewer pilots than other CS-based schemes.

Blockchain-Based Authentication Scheme with an Adaptive Multi-Factor Authentication Strategy

Authentication is of paramount significance to cybersecurity. However, most of conventional authentication schemes are implemented in a centralized mode, in which potential problems that could arise include single-point failure, the exposure of personal information, and the risk of identity theft. Additionally, static single-factor authentication schemes are unsuitable for dynamic environments like mobile applications. In order to tackle these difficulties, we propose a blockchain-based authentication scheme with an adaptive multi-factor authentication strategy. Our scheme features a blockchain-based authentication framework that prevents unauthorized information alteration and system corruption. Additionally, we design an adaptive multi-factor authentication strategy model to ensure trustworthy multi-factor authentication in dynamic scenarios. Last, we construct a Raft-based consensus model to select an authoritative leading node for rapid authentication. The security analysis demonstrates the effectiveness of the proposed scheme in effectively countering various forms of cyberattacks targeted at authentication systems, and experiments demonstrate its superior effectiveness and efficiency compared to existing studies.

Service failure monitoring via multivariate multiple linear regression profile schemes with dimensionality reduction

Service failures can be manifested through customer complaints in the form of negative reviews. It is vital for businesses to monitor the occurrence and severity of customer complaints to help balance service failure responses and costs. Existing monitoring schemes can detect complaint anomalies but fail to explain the reasons for these anomalies. We propose new multivariate multiple linear regression profile (MMLRP) based schemes to monitor the relationship between customer complaints and identified explanatory variables. Conventional MMLRP-based schemes suffer performance loss with more than five explanatory variables, and the fixed parameters drawn from a historical process are unsuitable for a changing review-generation process. As such, we apply dimensionality reduction techniques to the explanatory variables and incorporate the variability in new review samples. We use both simulation analysis and an airline service case study to show the effectiveness of our proposed MMLRP-based schemes in customer complaint anomaly detection and diagnosis.

Feasibility study of faecal sludge treatment by Geotube and jute tube-based technologies.

The decentralized standalone treatment of faecal sludge is a generally cumbersome, energy-intensive, and expensive process. Particle size enlargement, dewatering, and disinfection are the crucial unit operations that contribute to the major portion of the operational expenditure. Therefore, the present study took up the task to investigate and yield a natural alternative to each of the above-delineated unit operations. Crushed seed powders of Moringa, Guilandina bonduc, Nirmali, tamarind, soap nut, nutmeg, jackfruit, and custard apple were experimented as natural coagulants, while a double-stitched cow dung-lined jute tube was utilized as the media for solid-liquid separation. Finally, the powder of Neem and custard apple seed and leaf were considered herbal disinfectants for the separated liquid and solid fractions, respectively. The findings of the physicochemical and biological analyses of the above unit operations were compared with a conventional polymer-based Geotube-driven treatment scheme. The natural alternatives registered some promising outcomes. However, the combined treatment efficiency of 79.32% (mean value of TS, TSS, TDS, COD, and BOD percentage removal) for pollutant removal and disinfection could not exceed the value of 97.98% offered by the conventional method. Further, the financial comparison for processing a 10 KL batch of FS indicates that the conventional scheme is considerably cheaper (USD 7.95) than the herbal alternate (USD 22.1). Therefore, though the present findings depict promising alternatives for sustainable and eco-friendly faecal sludge treatment, the authors advocate conducting further research to address the existing challenges to facilitate their seamless implementation on a field-scale level.

Evaluation of different machine learning algorithms for LULC classification in heterogeneous landscape by using remote sensing and GIS techniques

Remote sensing land cover classification plays a crucial role in detecting changes, urbanization planning, mapping and monitoring land cover on earth surface. It is very challenging to get accurate result in remote sensing data because different classifiers are very much area dependent. Different classifiers such as decision tree (DT), K-nearest neighbor (KNN), artificial neural network (ANN), support vector machine (SVM), boosted decision tree (BDT), random forest (RF), classification and regression tree (CART) and maximum likelihood classifiers (MLC), have different accuracies for the same classes. Several studies have utilized remote sensing and GIS tools to investigate changes in land use and land cover (LULC) using different classifiers. Seasonal rivers which should be classified as water bodies are mostly classified as urban area with the conventional land cover classification schemes because spectral reflectance of these river bodies is similar to urban area due to stones present in their river bed. It is very difficult to distinguish between these river beds (which are mostly found in various districts of Uttarakhand, India) and urban area in remote sensing images. In this paper, we present a new method to distinguish these river beds with the urban area and to separate other classes easily. First of all different spectral indices such as NDVI, NDBI, EVI and MNDWI are extracted from a high resolution Sentinel-2 MSI image then these indices are integrated with Sentinel-2 MSI image for classification of different land cover classes by using four different machine learning classifiers such as RF, SVM, DT and CART. The obtained results confirm the performance strength of the suggested method as there is much improvement in accuracy.

A mechanically adjustable acoustic metamaterial for low- frequency sound absorption with load-bearing and fire resistance

ABSTRACTBroadband sound absorption is limited to discrete noise with abrupt peaks in the spectrum. Here, we proposed a mechanically adjustable acoustical metamaterials (AAMM) for low-frequency sound absorption with deep-subwavelength (0.025λ), which integrates Helmholtz resonators and Fabry–Perot (FP) tubes by precise modular design. The calculation results based on the theoretical model demonstrate that the broad low frequency (from 100 Hz to 500 Hz) tunability of the composite adjustable sound absorbing materials. The adjustable design scheme is further verified by numerical simulation. Then a multi-impedance adjustment method is proposed to improve the local optimal defect and make it have quasi-perfect sound absorption effect in the range of 120 Hz–348 Hz. The sound absorbing material sample can withstand 2.7 tons of dynamic load and 1300° high temperature, presenting superior compression and fire resistance compared to conventional porous sound absorbing materials and membrane acoustic metamaterials. This research on assembled machine-adjustable sound absorption material enriches the conventional acoustic metamaterial design scheme, further improves the space utilization rate, and provides an effective solution for dealing with low-frequency complex variable noise.

Hierarchical bi-level load frequency control for multi-area interconnected power systems

To overcome the shortcomings of proportional–integral (PI) control, a linear quadratic regulator (LQR) is proposed in this study, to determine the set points of the PI controllers in a two-level hierarchical manner. The application of the proposed scheme is studied on load frequency control (LFC) of a power system with multiple control areas (CAs). At the lower control level, a discrete PI controllers are used to regulate the frequency and tie-line powers of each of the CAs. To achieve an improved closed loop performance, the local PI controllers are optimally tuned using moth flame optimization (MFO) algorithm by minimizing the Integral Square Error (ISE) of the state errors with respect to local information and interactions with neighboring CAs. While at the upper control layer, an optimized finite-horizon LQR is applied as a guide to establish the optimal set-points of the lower level PI controllers. Since only few of the state variables can be accessed, Kalman filter (KF) is applied as an observer for the estimation of the remaining states. The efficacy of the proposed scheme is verified by implementing it on a three-CA system perturbed with a step and random net disturbances formed by combining the fluctuations in the output power of DFIG-based wind turbine generator and random load demand. From the simulation results, it is established that the proposed LQR-PI supervisory scheme has outperformed the conventional PI control scheme in optimality and stability.

Structure maps for MAX phases formability revisited

The extraordinary chemical diversity of MAX phases raises the question of how many and which novel ones are yet to be discovered. The conventional schemes rely either on executions of well designed experiments or elaborately crafted calculations; both of which have been key tactics within the past several decades that have yielded many of important new materials we are studying and using today. However, these approaches are expensive despite the emergence of high throughput automations or evolution of high-speed computers. In this work, we have revisited the in prior proposed “light-duty” strategy, i.e. structure mapping, for describing the genomic conditions under which one MAX phase could form; that allow us to make successful formability/non-formability separation of MAX phases with a fidelity of 95.5 %. Our results suggest that the proposed coordinates, and further the developed structure maps, are able to offer a useful initial guiding principles for systematic screenings of potential MAX phases and provide untapped opportunities for their structure prediction and materials design.

Single-pixel real-part and magnitude imaging system based on digital micromirror device

Single-pixel imaging (SPI) can capture images using a single-pixel detector. However, conventional SPI schemes only provide a magnitude image of the object, where the phase information is completely lost. Here, we present a dual-modal SPI (DMSPI) system capable of simultaneously capturing the real-part and magnitude images. Since the real-part image fuses the phase distribution, DMSPI has a strong information acquisition capability for all types of objects. The DMSPI system utilizes the two reflection arms provided by the digital micromirror device (DMD) to perform zero-frequency detection in one arm and bucket detection in the other to achieve dual-modal imaging. Benefiting from the unique modulation characteristics of DMD, the DMSPI system is simple and efficient, with high spatial resolution and fast imaging speed. Thus, it might find broad applications in biomedical diagnostics and industrial inspection.

Enhanced Non-Communication-Based Protection Coordination and Advanced Verification Method Using Fault Impedance in Networked Distribution Systems

In recent years, the networked distribution system (NDS), which is normally connected to the distribution line (DL), was actively studied as the topology of the future distribution system for reasons such as improving supply reliability, improving line utilization, and increasing the capacity of distribution generators (DGs). However, the NDS creates new issues in terms of protection coordination because of its bidirectional power flow and fault current flow. The issues associated with conventional protection schemes in the NDS include malfunction of protective devices due to bi-directional fault currents and failure of protection coordination due to communication failures between protective devices. When applying a conventional protection method in the NDS, the protection schemes become complicated, and there is a risk of protection coordination failure due to communication failure between protective devices. To solve this problem, this paper proposes an effective and innovative non-communication-based protection algorithm for protection coordination in the NDS. The proposed protection algorithm utilizes fault impedance characteristics, which allow not only determination of whether a fault occurred, but also the ability to identify the exact fault point. Therefore, the proposed method is expected to be sustainably utilized and contribute to developing protection schemes and devices in various system topologies and scenarios in the future. Additionally, this paper addresses the overall concept of hardware-in-the-loop simulation (HILS) and directly verifies the proposed protection algorithm using HILS. Therefore, this study establishes a sustainable foundation for future research on protection coordination using HILS.

Feedback Linearization Direct Torque Control With Zero-Sequence Current Regulation for DC-Biased VRM Drives

A feedback linearization direct torque control (FL-DTC) scheme with the ability of regulating zero-sequence current is proposed for the DC-biased vernier reluctance machines (DC-VRMs) to improve the steady and dynamic performance of drive system. First, an accurate original nonlinear model of DC-VRM is analyzed in synchronous rotating reference frame with three variables (i.e., the electromagnetic torque, the stator flux, and zero-sequence current). The saturation of self-inductance is considered to ensure the accuracy of model under different load. The virtual 0-axis flux <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ψ₀</i> is firstly defined in this paper. Then, a FL-DTC scheme with zero-sequence current regulation is proposed to improve torque and flux ripple and dynamic response performance. The system stability is verified by the Lyapunov theory. Finally, comparative experiments to conventional PI-based SVM-DTC scheme in steady state and dynamic process are carried out. The advantages of the proposed method are verified, including high control accuracy of torque and flux, fast dynamic response to load change and speed reversal, outstanding low-speed range performance and strong robustness to parameter uncertainties.

Vehicular Connectivity on Complex Trajectories: Roadway-Geometry Aware ISAC Beam-Tracking

In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling. Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries. To improve the tracking accuracy and communication quality of service (QoS) in vehicle to infrastructure (V2I) networks, it is essential to model the complicated roadway geometry. To that end, we impose the curvilinear coordinate system (CCS) in an interacting multiple model extended Kalman filter (IMM-EKF) framework. By doing so, both the position and the motion of the vehicle on a complicated road can be explicitly modeled and precisely tracked attributing to the benefits from the CCS. Furthermore, an optimization problem is formulated to maximize the array gain by dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account. Numerical simulations demonstrate that the roadway geometry-aware ISAC beamforming approach outperforms the communication-only-based and ISAC kinematic-only-based technique in tracking performance. Moreover, the effectiveness of the dynamic beamwidth design is also verified by our numerical results.

Cooperative NOMA-Based Spectrum Leasing With Multiple Secondary Users

This paper proposes a new cooperative nonorthogonal multiple access-based spectrum leasing (CNOMA-SL) framework for enhanced network performance. Herein, the system is comprised of a primary network and a secondary network with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -transmitting users. Primary and secondary users exploit an underlay spectrum leasing scheme to transmit signals to a relay station (RS), which forwards the signals to the corresponding receivers. Two phases of data transmission are assumed. In Phase I, the secondary transmitters employ uplink NOMA to transmit the symbols. The RS then uses downlink NOMA to broadcast the received symbols in Phase II. We employ the maximum ratio combining decoding scheme and propose a successive-interference-cancellation concept with ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n+1$</tex-math></inline-formula> ) stages to decode the symbols at the primary and secondary receiving ends, respectively. Considering a transmission power constraint imposed at the secondary transmitters, we derive the closed-form expressions for achievable rate and outage probability for both primary and secondary users. Simulation results manifest that, compared with the conventional cooperative orthogonal multiple access-based spectrum leasing scheme, the proposed CNOMA-SL exhibits improved performance in terms of both achievable rate and outage probability. It is also revealed that the system performs better when the RS takes the lease of the whole spectrum. Furthermore, if more interference power is allowed to the primary receiver, the secondary users reach higher achievable rates.

Design of Double Tank Liquid Level Control System Based on Fuzzy PID

Liquid level is one of the main parameters in industrial production process control. There are many problems such as interference and delay in the dual capacity water tank. It is usually difficult to meet the strict production requirements with the conventional PID control scheme. The corresponding transfer function is firstly deduced through the material balance equation in this article. By testing, the performance of conventional PID needs to be further improved. Then build the fuzzy PID model in Matlab/simulink and carry out simulation verification. By comparing with the PID simulation effect, the results show that under fuzzy PID control, the overshoot of the system is effectively reduced and the adaptability is strong. It reflects that the control strategy has a good reference value.

Joint Mobility Control and MEC Offloading for Hybrid Satellite-Terrestrial-Network-Enabled Robots

Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.

Energy-saving design and multi-objective optimisation of triple-column pressure-swing distillation configuration for separating n-propanol/benzene/water mixture with multiple azeotropes

The separation of ternary azeotropic mixtures with multiple azeotropes is often encountered in industrial wastewater treatment. This study explores separating the n-propanol/benzene/water mixture with three binary azeotropes and one ternary azeotrope via pressure-swing distillation (PSD). Two conventional PSD schemes are designed based on the ternary phase diagram. The multi-objective genetic algorithm facilitates the optimisation task for each sequence, taking the total annual cost, CO2 emissions, and thermodynamic efficiency as the objective functions. Then, the selection of the optimal solution from the Pareto front is performed using the technique for order preference by similarity to the ideal solution. Subsequently, energy-saving methods, including direct heat integration, heat pump, and heat exchanger network, are introduced into each optimal non-heat-integrated PSD process to address the drawback of tremendous total energy consumption. Finally, the proposed 12 energy-efficient PSD designs are compared with the conventional PSD cases in multiple aspects. The optimisation results highlight the importance of operating pressures as variables in the multi-objective optimisation problem. In addition, the W-B-N sequence shows superiority over the W-N-B sequence in decreasing cost and gas emissions and enhancing energy efficiency. The proposed energy-saving PSD design with direct heat integration, heat pump, and heat exchanger network gives the lowest cost amongst the 14 processes, being up to a reduction of 39% compared to the corresponding conventional PSD design.

A Novel Spacetime Boundary-Type Meshless Method for Estimating Aquifer Hydraulic Properties Using Pumping Tests

This article introduces a new boundary-type meshless method designed for solving axisymmetric transient groundwater flow problems, specifically for aquifer tests and estimating hydraulic properties. The method approximates solutions for axisymmetric transient groundwater flow using basis functions that satisfy the governing equation by solving the inverse boundary value problem in the spacetime domain. The effectiveness of this method was demonstrated through validation with the Theis solution, which involves transient flow to a well in an infinite confined aquifer. The study included numerical examples that predicted drawdown at various radial distances and times near pumping wells. Additionally, an iterative scheme, namely, the fictitious time integration method, was employed to iteratively determine the hydraulic properties during the pumping test. The results indicate that this approach yielded highly accurate solutions without relying on the conventional time-marching scheme. Due to its temporal and spatial discretization within the spacetime domain, this method was found to be advantageous for estimating crucial hydraulic properties, such as the transmissivity and storativity of an aquifer.

A novel approach to address source overprint and shadow effects in controlled-source audio-frequency magnetotelluric exploration

Source overprint and shadow effects can significantly affect controlled-source audio-frequency magnetotelluric (CSAMT) data and mask subsurface geologic structures; however, few solutions have been presented. Based on the traditional observation approach, we develop a new approach to solve this problem. An additional transmitter and measurement zone are introduced in the novel approach to constrain the anomalous body causing source overprint or shadow effects in the inversion. To test this approach, six experimental models are designed. Three-dimensional CSAMT responses on the six synthetic models have been simulated, and the results indicate that source overprint and shadow effects have a strong influence on the data. Strong source overprint and shadow effects are found at low frequencies. The distribution of the source overprint or shadow distortions is affected by the shape of the anomalies below the transmitter or between the transmitter and receiver. We evaluate our approach by comparing the inversion results obtained with the new and traditional approaches based on the six synthetic data sets. The results indicate that the new approach not only effectively resolves the anomalies causing source overprint or shadow distortions but also yields more accurate target results in terms of resistivity value, structure geometry, and position than the conventional scheme. The inversion of field data from Yanqing, China, further verifies the effectiveness of our approach. Compared with the traditional inversion, the new scheme inversion obtains results that are more consistent with the rock type revealed by a drillhole.

Analysis on Fairness and Efficiency of the 3-PLP LDM System Using a Normalized Channel Capacity

This article presents a novel approach to investigate efficient resource allocation in the layered division multiplexing (LDM) system, a key technology in next-generation broadcasting systems. The LDM system provides diverse services, resulting in different channel capacities for each service, unlike the conventional non-orthogonal multiple access scheme that aims for equal channel capacity among users. To achieve efficient resource allocation among services, we proposed a new method that normalizes the channel capacities allocated to different services while maintaining high fairness. Our proposed method involves normalizing the channel capacities of each service and finding the point where the sum of normalized channel capacities is maximized. This point can be obtained using the Lagrange multiplier method, ensuring a high normalized fairness index. Through simulations, we confirmed that the proposed approach maintains high fairness in the LDM system and achieves the highest normalized channel capacities compared to other transmission techniques. These findings provide valuable insights into resource allocation strategies for the LDM system, enabling a fair comparison of efficiency and performance within a multi-service environment.

Energy Efficient CH Selection Scheme Based on ABC and Q-Learning Approaches for IoUT Applications

Nowadays, the Internet of Underwater Things (IoUT) provides many marine 5G applications. However, it has some issues with energy efficiency and network lifetime. The network clustering approach is efficient for optimizing energy consumption, especially for underwater acoustic communications. Recently, many algorithms have been developed related to clustering-based underwater communications for energy efficiency. However, these algorithms have drawbacks when considered for heterogeneous IoUT applications. Clustering efficiency in heterogeneous IoUT is influenced by the uniform distribution of cluster heads (CHs). As a result, conventional schemes are inefficient when CHs are arranged in large and dense nodes since they are unable to optimize the right number of CHs. Consequently, the clustering approach cannot improve the IoUT network, and many underwater nodes will rapidly consume their energies and be exhausted because of the large number of clusters. In this paper, we developed an efficient clustering scheme to effectively select the best CHs based on artificial bee colony (ABC) and Q-learning optimization approaches. The proposed scheme enables an effective selection of the CHs based on four factors, the residual energy level, the depth and the distance from the base station, and the signal quality. We first evaluate the most suitable swarm algorithms and their impact on improving the CH selection mechanism. The evaluated algorithms are generic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and ABC. Then, the ABC algorithm process is improved by using the Q-learning approach to improve the process of ABC and its fitness function to optimize the CH selection. We observed from the simulation performance result that an improved ABC-QL scheme enables efficient selection of the best CHs to increase the network lifetime and reduce average energy consumption by 40% compared to the conventional ABC.