Transmission Problem Research Articles

Autonomous design of noise-mitigating structures using deep reinforcement learning.

This paper explores the application of deep reinforcement learning for autonomously designing noise-mitigating structures. Specifically, deep Q- and double deep Q-networks are employed to find material distributions that result in broadband noise mitigation for reflection and transmission problems. Unlike conventional deep learning approaches which require prior knowledge for data labeling, the double deep Q-network algorithm learns configurations that result in broadband noise mitigations without prior knowledge by utilizing pixel-based inputs. By employing unified hyperparameters and network architectures for transmission and reflection problems, the capability of the algorithms to generalize over different environments is demonstrated. In addition, a comparison with a genetic algorithm highlights the potential for generalized design in complex environments, despite the algorithms tending to predict local maxima. Furthermore, we examine the impact of hyperparameters and environment types on agent performance. The autonomous design approach offers generalized learning while avoiding restrictions to specific shapes or prior knowledge of the task.

Outage-guaranteed transmission for IRS-assisted FSO systems.

Intelligent reflecting surface (IRS)-assisted free space optical (FSO) communication can overcome line-of-sight and bring spatial diversity, which is considered to be a good complement to direct FSO links. This paper is interested in understanding the role of IRS-assisted FSO links when coexisting with direct FSO links. Due to the diverse nature of existing FSO channel models and the reliability requirements of IRS-assisted FSO systems, this paper aims to investigate the transmission problems of link selection and power configuration under the quality of service guarantee for link outage, which can be applicable to different channel models. Despite the inherent complexity of the problems, transmission schemes, i.e., sum rate transmission (SRT) and rate fairness transmission (RFT) are developed, which can achieve the respective optimal performance. Also, some insights for transmission schemes are provided. The results verify the efficacy of the proposed transmission schemes. The performance of the system may be improved by 40% and 41% for SRT and RFT, respectively.

Factor Relating to Behaviour in Managing Disposable Mask Waste in The Harbour of Batan City

The harbour which is a public place has the potential as a place for disease transmission, environmental pollution, or other health problems. The objective of the current research was to determine the factors relating to behavior in managing disposable. This type of research is observational analytic using a cross sectional design. Simple random sampling technique and a sample of 90 respondents. The research instrument used a questionnaire and used the Chi Square test and logistic regression (α = 0.05). The results of the study showed that the most important factor related to the management of garbage masks are knowledge with (p value = 0.004; p < 0.05; OR = 6.571; 95% CI = 1.566-25.929), attitude (p value = 0.049; p < 0.05; OR = 3.388; 95% CI = 1.006-11.407), and binding regulations (p value = 0.002; p < 0.05; OR = 0.098; 95% CI = 0.022-0.434). The conclusions of the current research is that there is a significant relationship between knowledge, attitude and binding regulations with mask waste management.

Hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade

The district integrated energy system has the characteristics of multi-layer and multi-subjects. Aiming at the problems of data privacy, conflict of interest, and mismatch of interaction power among these multi-subjects, a hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade is proposed. Firstly, the operational communication architecture of the district integrated energy system based on the cloud-pipe-edge-end is constructed to solve the problems of data transmission, data computation, and data privacy. Secondly, a two-layer distributed scheduling model of district integrated energy system considering waste heat recovery is established based on a laddered carbon trading mechanism. At the upper level, an optimal scheduling model targeting low-carbon and economic performance is developed by considering the participation of district integrated energy system in demand response and the purchase and sale of electricity. At the lower level, an optimal scheduling model considering waste heat recovery for subsystem energy-coupled equipment is developed. Then, the analytical target cascade is used to realize the decoupling of the two-layer model, and the efficient solution is achieved in the process of multiple iterations. Finally, by analyzing the arithmetic examples, it is verified that the proposed method can improve the economy of system operation by 4.38% and the absorption capacity of wind power by 9.89%, while protecting the privacy and interest claims of multiple parties.

L p regularity for elliptic transmission problems with two spectral parameters

ABSTRACT We study a family of elliptic transmission problems on heterogeneous unbounded domains posed in UMD spaces. In order to determine the strict solution, this work investigates the effect and relationships between two spectral parameters; the first parameter is in the differential equation, and the second is in the permeability condition on the interface. Necessary and sufficient conditions on the data are established to prove the maximal regularity of the solution by using semigroup theory and the properties of fractional powers of linear operators

Rooftop solar potential in micro, small, and medium size enterprises: An insight into renewable energy tapping by decision-making approach

Rooftop solar micro-power plants mitigate pollution and transmission problems in crowded Indian cities. The Indian government is exploring alternate solutions, and the micro, small, and medium enterprises (MSME) sector is also coming forward to opt for sustainable solutions. This study is focused on the MSME segment in India, which constitutes approximately 63 million entities. Installing a solar system for which a rooftop could be the best place to tap solar energy from these entities is required. Checking the rooftop solar potential of any MSME entity requires a suitable technique to decide which sector to target first and the further order. A unique methodology based on multi-criteria decision-making (MCDM) methods is presented to rank 10 MSME alternatives with seven criteria. It includes the measurement of alternatives and ranking according to compromise solution (MARCOS) to rank MSME sectors in India with rooftop solar potential. Weights of importance are assigned with three objective techniques and combined with the Bonferroni operator. The ranks attained are compared with different MCDM methods, and sensitivity analysis is performed while changing weights. Kendall and Spearman rank correlation coefficients are utilized to access the correlation levels between various strategies. Finally, Garrett’s ranking technique is used to finalize the different ranks. The results reveal that the MARCOS method performed well, and “textiles” ranks first, followed by “auto and engineering products.” As a result, the leading industrial sectors will help improve energy utilization while concentrating on renewable energy resources.

A Mobile AD Hoc Network Relay Method based on OLSR Protocol

Aiming at the reliability and performance problems of data transmission in the complex mobile AD hoc network environment, a relay method is designed and implemented in the unstable network environment with complex dynamics such as mobile, multi-hop and no center according to practical needs. By introducing a prior link-state routing protocol, OLSR is introduced. Using range value judgment, sliding window statistics and improved multi-point relay selection technology, it can effectively reduce the redundancy of relay node set, reduce the forwarding frequency of service data in the network, save the network overhead, and ensure the data transmission delay and quality in the large scale and dense mobile AD hoc network.

Optimization of underwater visible light communication transmission using multilevel regression modelling

This paper proposes the 32-Channel Dense wavelength Division Multiplexing (DWDM) system for Underwater Visible Light Communication (UVLC). The proposed DWDM-UVLC system performs the transmission of visible light data under different underwater ocean environmental conditions such as Pure sea, Clear ocean, and Coastal ocean water and ocean pollution such as oil spills, microplastics. The data transmission through visible light in underwater ocean environment is a challenging task due to attenuation in ocean water, which causes variations in water column parameters such as temperature, Ph, salinity, and chemical oil spill contents in water such as water with oil spills, and microplastics. The proposed dense wavelength Division Multiplexing (DWDM) system for Underwater Visible Light Communication (UVLC) is optimized using Bayesian optimized Multiple Regression Learning (BO-MRL) method. The BO-MRL based DWDM for UVLC mitigates the attenuation problems in data transmission, which is in the range of approximately 350m. The proposed DWDM-UVLC system is evaluated through the Performance metrics such as Q-factor, Minimal BER and proper spectral eye characteristics. The proposed BO-MRL based DWDM consists of non-return-to-zero (NRZ) modulation, which performs better in 320 Gbps, transmission in the range of 200m and 350m, and achieves the maximum Q-factor of 19.2364 with a minimal BER of 9.16489 e−083.

Numerical Recovering of Space-Dependent Sources in Hyperbolic Transmission Problems

A body may have a structural, thermal, electromagnetic or optical role. In wave propagation, many models are described for transmission problems, whose solutions are defined in two or more domains. In this paper, we consider an inverse source hyperbolic problem on disconnected intervals, using solution point constraints. Applying a transform method, we reduce the inverse problems to direct ones, which are studied for well-posedness in special weighted Sobolev spaces. This means that the inverse problem is said to be well posed in the sense of Tikhonov (or conditionally well posed). The main aim of this study is to develop a finite difference method for solution of the transformed hyperbolic problems with a non-local differential operator and initial conditions. Numerical test examples are also analyzed.

A comprehensive study on the romax-based comprehensive shaping method for skewed cylindrical gears

Abstract Tooth profile modification is one of the effective ways to solve the vibration and noise problems in gear transmission. This article is based on the dynamic Romax Designer software for simulation and studies the micro modification problem of helical cylindrical gears in a certain reducer. Based on the analysis of the limitations of tooth profile modification and tooth orientation modification methods, a comprehensive tooth profile modification method is proposed. The simulation results show that the transmission error, load distribution, meshing situation, and service life of the gear have improved after adopting the comprehensive modification method, thereby improving the transmission accuracy, reliability, and durability of the gear. This method can provide reference and reference for gear modification of reducers.

A Novel Collaborative SRU Network With Dynamic Behaviour Aggregation, Reduced Communication Overhead and Explainable Features.

Leakage and tampering problems in collection and transmission of biomedical data have attracted much attention as these concerns instigates negative impression regarding privacy, security, and reputation of medical networks. This article presents a novel security model that establishes a threat-vector database based on the dynamic behaviours of smart healthcare systems. Then, an improved and privacy-preserved SRU network is designed that aims to alleviate fading gradient issue and enhance the learning process by reducing computational cost. Then, an intelligent federated learning algorithm is deployed to enable multiple healthcare networks to form a collaborative security model in a personalized manner without the loss of privacy. The proposed security method is both parallelizable and computationally effective since the dynamic behaviour aggregation strategy empowers the model to work collaboratively and reduce communication overhead by dynamically adjusting the number of participating clients. Additionally, the visualization of the decision process based on the explainability of features enhances the understanding of security experts by enabling them to comprehend the underlying data evidence and causal reasoning. Compared to existing methods, the proposed security method is capable of thoroughly analyzing and detecting severe security threats with high accuracy, reduce overhead and lower computation cost along with enhanced privacy of biomedical data.

On some Robin–transmission problems for the Brinkman system and a Navier–Stokes type system

The aim of this paper is twofold. The first goal is to give well‐posedness results for a Robin–transmission problem and a Robin–Dirichlet problem for the Brinkman system in bounded Lipschitz domains in . The Robin–Dirichlet problem appears as a special limit case of the Robin–transmission problem. This aim is achieved by employing the layer potential theoretical methods. The second goal is to provide a well‐posedness result for a Robin–transmission problem and an existence result for the Robin–Dirichlet problem for a Navier–Stokes type system in bounded Lipschitz domains in . Again, the Robin–Dirichlet problem appears as a special limit case of the Robin–transmission problem. This aim is achieved by using the well‐posedness results in the linear case combined with a fixed point argument. In order to illustrate our theoretical results, we also consider a special case of boundary value problems of Robin–Dirichlet type, which models a fluid flow in a porous square cavity. We solve numerically this problem.

Rate-Distortion-Perception Optimized Neural Speech Transmission System for High-Fidelity Semantic Communications.

We consider the problem of learned speech transmission. Existing methods have exploited joint source-channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission. In this paper, we propose a novel neural speech transmission framework named NST. It can be optimized for superior rate-distortion-perception (RDP) performance toward the goal of high-fidelity semantic communication. Particularly, a learned entropy model assesses latent speech features to quantify the semantic content complexity, which facilitates the adaptive transmission rate allocation. NST enables a seamless integration of the source content with channel state information through variable-length joint source-channel coding, which maximizes the coding gain. Furthermore, we present a streaming variant of NST, which adopts causal coding based on sliding windows. Experimental results verify that NST outperforms existing speech transmission methods including separation-based and JSCC solutions in terms of RDP performance. Streaming NST achieves low-latency transmission with a slight quality degradation, which is tailored for real-time speech communication.

FACTS Devices as a Solution to Power Industries Problems: A Review

With an ever-increasing demand for power and establishment of new industries with high load demand, the need to for constant upgrade of powers system network is high. Such transmission networks are prone to external disturbances from loads, environment and other sources which leads to low power quality. Sudden application or removal of large loads. Large loads are known to draw more reactive power than the generated reactive power which lead to reactive power imbalance which can lead to total system collapse. In this paper, a critical review of how Flexible Alternating Current Transmission Systems (FACTS) devices are used to mitigate such issues to ensure power quality is done. Previous work on the integration of different FACTS devices were review to establish the advantage of FACTS devices over conventional solutions to power transmission problems.

A Joint Communication and Computation Design for Probabilistic Semantic Communications.

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model represents large-sized data through substantial knowledge graphs, utilizing shared probability graphs between the users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of the equivalent rate of all users, considering the total power budget and semantic resource limit constraints. The computation load considered in the PSC network is formulated as a non-smooth piecewise function with respect to the semantic compression ratio. To tackle this non-convex non-smooth optimization challenge, a three-stage algorithm is proposed, where the solutions for the received beamforming matrix of the BS, the transmit power of each user, and the semantic compression ratio of each user are obtained stage by stage. The numerical results validate the effectiveness of our proposed scheme.

A conservative discontinuous-Galerkin-in-time (DGiT) multirate time integration framework for interface-coupled problems with applications to solid–solid interaction and air–sea models

In this paper we extend the DGiT multirate framework, developed in Connors and Sockwell (2022) for scalar transmission problems, to a solid–solid interaction (SSI) problem involving two coupled elastic solids and a coupled air–sea model with the rotating, thermal shallow water equations. In so doing we aim to demonstrate the broad applicability of the mathematical theory and governing principles established in Connors and Sockwell (2022) to coupled problems characterized by subproblems evolving at different temporal scales. Multirate time integration algorithms employing different time steps, optimized for the dynamics of each subproblem, can significantly improve simulation efficiency for such coupled problems. However, development of multirate algorithms is a highly non-trivial task due to the coupling, which can impact accuracy, stability or other desired properties such as preservation of system invariants. DGiT provides a general template for multirate time integration that can achieve these properties. To elucidate the manner in which DGiT accomplishes this task, we fully detail each step in the application of the framework to the SSI and air–sea coupled problems. Numerical examples illustrate key properties of the resulting multirate schemes for both problems.

Multi-scale terahertz image reconstruction

This paper proposes a novel terahertz (THz) image recovery algorithm and a new THz image dataset is publicly available. Because of transmission noise, artificial errors and problems with diffraction phenomena are among the main problems that cause degradation of THz images. The point spread function (PSF) in real transmission is first obtained using a terahertz imaging system. Experiments can generate degraded images and clean image datasets for supervised training of neural networks via terahertz PSF. A Depthwise Dense Instantiation Normalization Block (DwDIN Block) has been proposed to recover the datasets. We constructed a powerful multi-stage network (DwDINet) at each scale by utilizing the DwDIN module. For input and output, the results of each stage of the network and the original images are taken as input into the next layer in order to enhance the background information. DwDINet achieves state-of-the-art (SOTA) results on the two main THz datasets. To verify the generality of the model, we performed experiments on various image recovery tasks. According to the experimental results, the proposed model is of great potential for the field of low-level image clarification.

Impedance operator of a curved thin layer in linear elasticity with voids

We consider a two‐dimensional transmission problem of linear elasticity with voids in a fixed domain coated by a curved thin layer . Our aim is to model the effect of the thin layer on the fixed domain by an impedance boundary condition. For that, we use the techniques of asymptotic expansion to approximate the transmission problem by an impedance problem set in the fixed domain , and we prove an error estimate for the approximate impedance problem.

Channel prediction method based on the data-driving for distribution automation main station

A data-driven channel prediction method for distribution automation master is proposed to address the poor quality of communication network and communication system transmission problems in distribution network communication. In this paper, an adaptive broad learning network (ABLN) consisting of a standard broad learning network and a hybrid learning network is introduced to predict the channel state information of the communication system. Among them, the hybrid learning network is used to solve the ill-conditioned solution problem when estimating the output weight matrix of the standard broad learning network. Therefore, the ABLN produces sparse output weight matrices and provides excellent prediction performance. In the simulation analysis, the outdoor and indoor scenes are considered based on OFDM system. The prediction performance of ABLN is subsequently evaluated in one step prediction and multistep prediction. The results show that for the prediction performance is concerned, the maximum improvement of ABLN is about 96.49% as compared to other evaluation models, indicating that the CSI is effectively predicted by the ABLN to support the adaptive transmission of the main station of the distribution automation and to satisfy the quality of the communication network of the distribution network.

Analysis of the exponential stability of a beam-string-beam transmission problem with local damping on the string

We consider a beam-string-beam problem, where two undamped beams are coupled to a locally damped string (first by a damping of frictional type and then by one of Kelvin–Voigt (K–V) type) by transmission conditions. We show that for this structure, the dissipation produced by the frictional part is strong enough to produce an exponential decay of the solution no matter how small is its size, but the semigroup associated with the system is not exponentially stable when we apply a some localized K–V damping on the string, given by a suitable discontinuous function and certain condition under the length of the parts of the structure. However, when we impose hinged conditions on the beams on the interface points, the dissipation produced by a some localized K–V damping, given by a suitable continuous function, is strong enough to produce an exponential decay of the system solution. For the proof of these three results we use a frequency-domain method of semigroup theory, where in the proofs of exponential stability we also use a contradiction argument.