Energy Constraints Research Articles

Cooperative optimal train operation algorithm for utilizing regenerative braking energy

The efficient use of regenerative braking energy between trains can significantly reduce the net traction energy consumption in urban rail transit systems, and has attracted considerable attention in recent years. An effective way of using regenerative braking energy is to adjust the train's coasting or cruising phase to an acceleration phase to match the braking phase of a nearby train within the same substation. This paper formulates the train driving phase adjustment problem as a cooperative optimal control problem, where a novel energy constraint is introduced to ensure efficient use of regenerative braking energy during the adjusted phase with optimal traction effort. By applying the control parameterisation method and elaborate handling of the energy constraint and the non-smooth control bound constraint, we transform the formulated problem into a nonlinear programming problem. We then use the sequential quadratic programming algorithm to solve the resulting problem, with the gradients of the cost and constraint functions computed using the sensitivity method. Numerical examples based on real-world data for a subway line show the effectiveness of the proposed method and its advantages over a genetic algorithm in terms of energy savings and computational efficiency.

Energy efficiency assessment of wastewater treatment plants in China based on multiregional input–output analysis and data envelopment analysis

Wastewater treatment plants (WWTPs) play a pivotal role in natural water recycling and safeguarding the water security of approximately 42% of the world's population, yet they confront challenges posed by escalating energy consumption and regional energy constraints. In this study, we developed an evaluation framework that integrated regional energy scarcity with the operation conditions of WWTPs to quantify their energy efficiencies by applying multiregional input–output (MRIO) analysis and data envelopment analysis (DEA). A case study using this framework was conducted on 3776 WWTPs in 30 provinces in China. The results showed that the average scarcity-based energy efficiency level of WWTPs in China was low, of which those in northern provinces were higher compared to southeastern provinces. Furthermore, only 24 WWTPs in China attained the highest efficiency levels, while the rest had different degrees of input redundancy, indicating an energy-saving potential of 9.05 × 109 kWh. This paper enables the development of optimal efficiency strategies for WWTPs in different provinces considering the potential energy scarcity risks caused by trading activities.

Energy Based Route Prioritization for Optimum Multi-path Selection

Energy–aware routing in wireless ad hoc networks is one of the demanding fields of research. Nodes of the network are battery operated that are difficult to recharge and replace, that's why while developing a routing protocol energy consumption metric should always be at high priority. Nodes of mobile ad hoc networks are distributed in different directions forming arbitrary topology instantly. To propose an energy-efficient routing protocol for such a dynamic, self-organized, self-configured, and self-controlled network is certainly a challenge and an open research problem. Energy constraints and mobility leading to link breakage are the motivating factors behind the development of the proposed Optimized Priority-based Ad Hoc on Demand Multi-path Distance Vector Energy Efficient Routing Protocol (OPAOMDV-EE). The routing protocol added three fields (CE, MAX_E, MIN_E) to the traditional AOMDV RREQ and RREP packets, which are further used for calculating total priority field value. This value is used by the source node for selecting an optimal prioritized energy-efficient route. The proposed OPAOMDV-EE protocol has been simulated on Network Simulator-2 (NS-2) for two different scenarios that prove the effectiveness of OPAOMDV-EE in terms of various performance metrics with reduced energy consumption.

Techno-Economic Feasibility Study of a 1.5 MW Grid-Connected Solar Power Plant in Bangladesh

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degradation, limited fossil fuel reserves, and volatile product costs, renewable energy sources are gaining momentum globally. Our research focuses on a grid-connected solar PV system model at Char Jazira, Lalpur, Natore, Rajshahi, Bangladesh. Through PVsyst 7.1 simulation software, we assess the performance ratio (PR) and system losses, revealing an annual solar energy potential of 3375 MWh at standard test condition (STC) efficiency. After considering losses, the system generates 2815.2 MWh annually, with 2774 MWh exported to the grid. We analyze an average PR of 78.63% and calculate a levelized cost of energy (LCOE) of 2.82 BDT/kWh [1 USD = 110 BDT]. The financial assessment indicates a cost-effective LCOE for the grid-connected PV system, with an annual gross income of 27,744 kBDT from selling energy to the grid and operating costs of 64,060.60 BDT/year. Remarkably, this initiative can prevent 37,647.82 tCO2 emissions over the project’s 25-year lifespan.

Correlation between water pollution and the incidence of common cancers Wuyingshan community as an example

The reasons for the significant decrease in cancer incidence in the past decade among residents of Wuyingshan Community, Tianqiao District, Jinan City, Shandong Province, where the author lives, are certainly comprehensive and multifaceted. Due to time and energy constraints, this paper takes the perspective of the creation of a National Sanitary City in Jinan and the widespread use of tap water purification equipment by residents in the Wuyingshan Community, and uses the sample sampling method and comparative research method to indirectly prove the relationship between water pollution and the incidence of common cancers among residents in the Wuyingshan Community by investigating the effects of the quality of residents drinking water on residents tumors. The results of the study show that there is a positive correlation between water pollution and the incidence of common cancers, and that the creation of the National Sanitary City and its continuous management of water pollution effectively reduce the incidence of common cancers in the community, and that the residents contribute to the further purification of tap water; on the other hand, residents who do not pay attention to the quality of drinking water are prone to the occurrence of common cancers. In order to sustainably reduce the level of urban water pollution and maintain drinking water quality that meets internationally accepted health standards, this paper makes several recommendations from the dimensions of water quality legislation, production and living needs, water quality monitoring, and sewage treatment.

Application of deep learning in medical imaging segmentation

The increasing demand for segmentation of lesions in medical images necessitates research on automatic segmentation. Manual segmentation is inefficient due to training time and energy constraints. Deep learning-based image segmentation technology can improve efficiency and aid in diagnosing conditions. This technology provides accurate and detailed data support for clinical medicine, making it a crucial field in medical image processing. This essay introduces image segmentation and its classification, and explains the differences between two segmentation methods, semantic segmentation and instance segmentation and their respective application fields. Additionally, it introduces several well-known deep neural networks for segmenting medical images using deep learning. Regarding the models, this article introduces the structure and characteristics of each model as well as their respective advantages and disadvantages. This essay also introduces examples of deep learning using different deep neural network models to segment specific medical images, including image segmentation based on FCN for the heart and U-Net for the kidneys.

Optimal Attack Energy Allocation Against Remote Estimation in Cyber-Physical Systems

This paper focuses on designing an optimal attack energy allocation strategy to disrupt the performance of remote state estimation with unknown gains for cyber-physical systems (CPSs). To deal with the unknown gains, the Cramér-Rao Bound (CRB) is introduced to obtain a lower bound of the secondary moment of the estimate error. Then, by solving an optimization problem that maximizes the average CRB over a finite time horizon with energy constraint, an optimal attack energy allocation strategy is derived. Compared with the existing works, the rigorous assumption that the attacker has full knowledge of the system is removed, and the computational complexity of solving the optimization problem is significantly reduced. Finally, numerical results validate the presented strategy.

Distributed Anti-Eavesdropping Fusion Estimation Under Energy Constraints

This paper studies the distributed fusion estimation problem with energy-constrained sensors in the presence of eavesdroppers, where smart sensors send their local estimates to a remote fusion center (FC). To enhance privacy level, a novel encryption strategy is proposed by establishing an optimization problem, where the optimization objective is constructed by maximizing a combination of the fusion terminal estimation error covariance and the cost of encryption process in finite time domain. Meanwhile, the established problem is decomposed into several independent sub-optimization problems under a relaxation condition, and a sufficient condition that depends on the system parameters is derived such that the sub-optimization problems have optimal solutions. In this case, an optimal encryption strategy is designed with an analytical solution. Finally, a simulation example is given to show the effectiveness of the proposed methods.

Multi-User Goal-Oriented Communications With Energy-Efficient Edge Resource Management

Edge Learning (EL) pushes the computational resources toward the edge of 5G/6G network to assist mobile users requesting delay-sensitive and energy-aware intelligent services. A common challenge in running inference tasks from remote is to extract and transmit only the features that are most significant for the inference task. From this perspective, EL can be effectively coupled with goal-oriented communications, whose aim is to transmit only the information relevant to perform the inference task, under prescribed accuracy, delay, and energy constraints. In this work, we consider a multi-user/single server wireless network, where the users can opportunistically decide whether to perform the inference task by themselves or, alternatively, to offload the data to the edge server for remote processing. The data to be transmitted undergoes a goal-oriented compression stage performed using a convolutional encoder, jointly trained with a convolutional decoder running at the edge-server side. Employing Lyapunov optimization, we propose a method to jointly and dynamically optimize the selection of the most suitable encoding/decoding scheme, together with the allocation of computational and transmission resources, across all the users and the edge server. Extensive simulations confirm the effectiveness of the proposed approaches and highlight the trade-offs between energy, latency, and learning accuracy.

Optimum Piezo-Electric Based Energy Harvesting for Low-Power Wireless Networks with Power Complexity Considerations

Low-power wireless sensing-based networks suffer from many constraints and challenges. In this research work, efficient power source has been designed to provide the need of energy for the Wireless Sensor Networks (WSNs) and Wireless Body Area Networks (WBANs). The energy sources are the main challenge and constraint these wireless networks applications. This paper discusses recent researcher’s works which considered the energy constraints of the WSN and WMSN with their proposed security techniques. The main idea of this presented work is the energy harvesting through extracting the electrical energy from the audio/acoustic signal/energy, this utilized audio/acoustic source in this scenario is the disk jockey. To maximize the produced power from the proposed acoustic energy harvesting Piezo-based several parameters is studied. The parameters are considered in this research work are the method of Piezo-transducers connections, the distance of sound source, the sound intensity variation and the sound concentrating tube length. These tubes are mounted on slim diaphragm two maximize the energy harvesting. The piezoelectric transducer array scenario is designed using four piezoelectric transducers utilizing different connect ion methods, series, parallel and in hybrid. Several practically experiments are performed on the presented different scenarios to evaluate the proposed energy harvesting efficiency. These experiments reveal that the superiority of the proposed acoustic energy harvesting technique with low power complexity wireless networks and suitable with the different presented scenarios.

Urban water resources management with energy constraint and carbon emission intensity under uncertainty: A dual objective optimization model

Water production, utilization and recycle in urban system associated with a large amount of energy consumption and carbon emission that has increasingly international attention under sustainable development goals. In this study, a comprehensive urban water resources system management model is proposed under water-energy-carbon nexus and multiple uncertainties. The water-energy-carbon nexus was reflected in the processes of water extraction, water production and sewage treatment, and two objectives of maximizing the system benefit and minimizing carbon emission is transformed into two linear programming problem through the dual-objective interval fractional programming. Considering the interaction among water supply/demand, pollutants discharge control, energy consumption and self-energy utilization, indirect carbon emission as the main constraints, the developed model is applied to a typical water and energy sources shortage area of Qingdao city in north China. Multiple scenarios related to energy consumption and carbon emission control are designed to search the relationship among energy consumption, carbon emission and water resources allocation schemes. The results indicated that water demand as the main driving factor of energy consumption and carbon emission in urban water system cannot be directly limited through water supply structure adjustment. Through introducing self-energy supply (sludge utilization) and energy consumption control, water resources allocation schemes and water supply structure would be changed to improve energy saving potential with an obvious decrease trend of carbon emission intensity from 0.7921 tCO2/MWh to 0.5544 tCO2/MWh. The model could effectively provide reasonable urban water system management strategies under different water allocation demands and carbon emission challenges scenarios and deal with multiple uncertainties in decision-making process.

Energy-efficient algorithms for lossless data compression schemes in wireless sensor networks

Wireless sensor networks (WSNs) are reliant on limited power resources, primarily provided by small batteries within sensor nodes. Inefficient energy management within these networks can lead to premature battery depletion during data transmission between sensor nodes, significantly impacting network longevity. Data compression emerges as a viable strategy to mitigate energy consumption by reducing data size before transmission and employing various compression and decompression techniques. This work presents a comparative analysis of data compression algorithms tailored for WSNs. It studies and enhances two adaptive lossless data compression techniques, namely ‘Adaptive Lossless Data Compression’ (ALDC) and ‘Fast and Efficient, Lossless Adaptive Compression System’ (FELACS), as means to effectively manage energy consumption in wireless sensor networks. ALDC and FELACS algorithms encode differences between consecutive data readings, thereby reducing the number of bits required for encoding. ALDC employs Huffman coding, while FELACS leverages the Golomb-Rice coding method. Encoding data samples by using three Huffman tables interchangeably as an enhancement of the ALDC algorithm, resulted in an improvement in energy saving from 73 % to 77 %. Analysis of FELACS unveiled the impact of natural phenomena-induced anomalies on measured data, identified as outliers. The outliers disrupt data patterns and ranges, subsequently altering the optimal coding parameters for data samples, resulting in encoding and decoding errors. This study proposes a robust method for identifying and replacing outliers within sensor data, significantly enhancing compression performance. A reduction of variations in dataset patterns facilitated more accurate sampling and encoding of data. Consequently, fewer bits are required to encode data samples, rendering the algorithm energy-efficient and suitable for applications demanding error-free data recovery or meticulous error analysis. The proposed method was successfully applied to the modified ALDC algorithm, exhibiting efficient performance. An optimum block size of sampled data was discovered for Fishnet relative humidity deployment ensuring efficient transmission of environmental data real-world sensor network deployments like Fishnet, Lucerne, and Le Genepi. These findings underscore the potential for significant energy savings and improved data accuracy through adaptive lossless data compression techniques, making them valuable assets for applications with stringent energy constraints or demanding data integrity.

Optimal tracking performance of networked control systems under communication constraints

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output discrete-time communication-constrained systems by thinking about Denial of Service (DoS) attacks, codecs and additive Gaussian white noise under energy constraints. The non-cooperative relationship between DoS attacks and intrusion detection systems (IDS) is analyzed using repeated game theory. A penalty mechanism is constructed to force the attackers to adopt a cooperative strategy, thus improving the system performance. Partial factorization and spectrum decomposition are used to provide the OTP for systems. The results demonstrate that the systems’ OTP are linked to intrinsic characteristics like non-minimum phase zeros and unstable poles. Finally, concrete examples are shown that the results are accurate.

Interpolating meshes of arbitrary topology by Catmull–Clark surfaces with energy constraint

Interpolating meshes of arbitrary topology by Catmull–Clark surfaces with energy constraint

Parametric studies on pretreatment of lignocellulosic biomass via deep eutectic solvents: Enhancing densified pellet quality

Densified pellets derived from lignocellulosic biomass are attractive candidates for alleviating energy constraints and replacing fossil fuels. However, the undesirable properties of lignocellulosic biomass and the resulting high costs of handling have hampered its widespread application. In the present study, we introduce a pretreatment strategy for lignocellulosic biomass to produce high quality densified pellets. In this process, the structure of lignocellulosic biomass was deconstructed and lignin was regenerated in situ, resulting in a homogeneous cellulose-lignin slurry. The resulting densified pellet exhibits higher density (up to ∼1200 kg/m3), strong mechanical strength (maximum increase to ∼12 N/mm2), better water resistance (decreased from 15% to 10%), and improved combustion characteristics (enhanced nearly twice). The improved performance indicates that the combination of deep eutectic solvent treatment with densification is an efficient way to develop processing schemes for sustainable and efficient utilization of lignocellulosic biomass.

The decarbonization pathway of power system by high-resolution model under different policy scenarios in China

Emission reduction from the coal power sector is vital for achieving carbon mitigation targets in China. In this study, we explore feasible pathways and economic cost of power system transition under different policy combination scenarios. We use a high-resolution power system model which is coupled with capacity planning and operation simulation to project the transition. We find that: (1) Without other policies, the single carbon market policy will lead to a potential rebound in emissions during the transition of the power industry. After 2045, a reduction in the proportion of free quotas and an increase in emission cost will enhance the emission reduction effectiveness of the market. (2) When policy measures are not sufficiently robust, even with a high proportion (80%) of renewable energy deployment requirement, there is a risk of rebound in coal power investments and subsequent emissions after 2050. (3) The achievement of carbon neutrality can only be realized by constraining the carbon budget within the boundaries of climate goals and other policy constraints. And carbon neutrality, compared to a high proportion (90%) of clean energy constraints, would increase total costs by at least 6%.

Unimodular Waveform Design for the DFRC System with Constrained Communication QoS

In this study, we investigated two waveform design problems for a dual-functional radar communication (DFRC) system, taking into consideration different constrained communication quality-of-service (QoS) requirements. Our objective was to minimize the mean-square error (MSE) of radar beampattern matching as the cost function. To this end, the multi-user interference (MUI) energy constraint and constructive interference (CI) constraint were, respectively, formulated to ensure the communication QoS. It is important to note that we designed a strict per-user MUI energy constraint at each sampling moment to achieve more accurate control over communication performance. Additionally, we introduced a constant-modulus constraint to optimize the efficiency of the radio frequency (RF) amplifier. To tackle the nonconvex waveform design problems encountered, we employed the alternative direction methods of multipliers (ADMM) technique. This allowed us to decompose the original problem into two solvable subproblems, which were then solved using the majorization–minimization (MM) method and geometrical structure. Finally, we obtained extensive simulation results which demonstrate the effectiveness and superiority of the proposed algorithm.

High performance SAW resonator with spurious mode suppression using double-layer electrode transverse modulation

This work aims to solve the problem of tradeoff between various properties and spurious mode suppression in surface acoustic wave (SAW) resonators. A high-angle rotated Y-cut LiNbO3 (LN)/SiO2/Si multilayered structure was proposed to balance the electromechanical coupling coefficient (K 2) and temperature coefficient of frequency (TCF), and the propagation characteristics of Rayleigh mode were simulated by the finite element method. For the widely existing spurious modes, the shear-horizontal wave and longitudinal modes were eliminated by optimizing the cut angle of LN and electrode thickness, and a method of double-layer electrode transverse modulation was proposed to suppress the transverse modes. This method reduces the mass loading effect by replacing the electrode from Cu to Cu/Al. Moreover, the Al thicknesses in different regions are changed to perform the transverse modulation, and thus a widespread suppression of transverse modes is achieved by exciting the piston mode and enhancing the energy constraint, with a significant improvement on quality factor at the resonance frequency. Eventually, the spurious-free SAW resonator has the K 2 of 9.5% and the TCF close to zero. This work provides a feasible scheme for the design of high performance SAW resonators with spurious mode suppression.

Influence of modified gravity on self-gravitating stellar objects

This paper focuses on studying the self-gravitating spherical compositions enclosing anisotropic fluid distribution in the context of f(G,T) theory. The modified field equations representing anisotropic source are formulated, and then the MIT bag model equation of state is used to contribute the participation of bag constant. The mathematical equations are thoroughly computed using a quadratic f(G,T)=G2+ħT model (where ħ indicates the coupling parameter). We utilize the Finch-Skea ansatz to examine the compact objects Her X-I, Vela X-I, PSR J1614-2230, LMC X-4 and 4U1820-30. This solution facilitates in determining the behavior of physical factors, anisotropic source and energy constraints corresponding to the above-mentioned stars graphically. We construct the graphs by following two distinct values of the coupling parameter throughout. Some other important factors such as mass, redshift and compactness are also discussed. To examine the stable character, we use three strategies, i.e., causality condition, Herrera cracking approach and adiabatic index. Interestingly, for both the values of ħ, all the considered candidates signify the viable as well as stable behavior.

Physical analysis of anisotropic compact stars in f(𝒬) gravity

The main objective of this study is to investigate the stability and viability of anisotropic compact stellar objects using the Krori–Burua solutions in [Formula: see text] gravity, where [Formula: see text] is a nonmetricity scalar that explains the gravitational effects. We use a static spherical metric in the inner region and Schwarzschild spacetime in the outer region of the Her X-I, SAX J 1808.4-3658 and 4U1820-30 compact stars to investigate their physical properties. By using observed values of the radius and mass of the considered compact stars, the unknown parameters are determined. We use a particular model of this theory to examine the behavior of energy density, pressure components, anisotropy, equation of state parameters and energy constraints in the interior of the proposed stellar objects. The Tolman–Oppenheimer–Volkoff equation is used to analyze the equilibrium state of these stars and causality condition, Herrera cracking method, adiabatic index methods are used to determine their stability. It is found that revolutionary technique sheds new light on the development of observationally accurate gravity model.