Loading Algorithm Research Articles

A new algorithm for load shifting operation of water pumping stations

Su dağıtım sistemlerinin işletilmesindeki en yüksek gider kalemini, pompalama için kullanılan enerji maliyeti oluşturmaktadır. Çok zamanlı elektrik tarifesi ve kentsel su talebi ile bağlantılı olarak, su dağıtım sistemlerindeki istasyonlara ait pompalama çizelgesinin optimizasyonu problemi matematiksel olarak modellenebilir. Bu çalışmada, pompalama enerji maliyetini düşürmeye yönelik olarak, pompalama çizelgesinin optimizasyonu problemini çözmek için yeni bir algoritma (SDPA-sadeleştirilmiş dinamik programlama algoritması) elde edilmiştir. Yapılan simülasyonlarda SDPA’nın, konvansiyonel su seviyesi kontrolü (CWCL-conventional water level control) yaklaşımının kullanılmasıyla oluşan enerji maliyetinde, geliştirilmiş dinamik programlama algoritması (IDPA-improved dynamic programming algorithm) yaklaşımına kıyasla daha fazla tasarruf elde edebileceği sonucuna ulaşılmıştır. Ayrıca SDPA, IDPA yaklaşımında karşılaşılan karar periyodu küçülürken pompa çalışma frekansının artması probleminin de önüne geçerek, pompa açma-kapama karar sayısını oldukça düşük tutmayı başarmıştır. SDPA, Sakarya ilindeki bir su dağıtım sistemine ait bir pompalama istasyonuna uygulanmış ve puant periyodundaki enerji talebini önemli ölçüde gece periyoduna kaydırarak, CWLC yaklaşımına kıyasla enerji maliyetinde yaklaşık %12’lik bir tasarruf sağlamıştır. Su dağıtım sistemlerine ait pompalama istasyonlarında ilave yatırım yapmaksızın, sadece pompalama çizelgesinin yapılandırıldığı bu algoritmanın kullanılmasıyla, önemli ölçüde enerji tasarrufu sağlanabileceği görülmüştür.

Day-ahead combined economic and emission dispatch with spinning reserve consideration using moth swarm algorithm for a data centre load

Dynamic combined economic and emission dispatch is an important task in the power system that examines the optimal allocation of power generation resources that yield the least possible fuel and emission costs. In this study, the Moth Swarm Algorithm has been proposed for solving the combined economic and emission dispatch problem for a 24-hour period. The model has been implemented on a test system made up of a combination of thermal and solar photovoltaic plants, while also considering spinning reserve allocation. The results obtained are presented in comparison with commonly used state-of-the-art methods like Moth Flame Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, and Tunicate Swarm Algorithm. Two test systems have been considered in the model implementation. The first system consists of a combination of six thermal plants and thirteen solar plants whose load demand is the hourly energy demand of an anonymous data centre in South Africa. The second test system is made up of three thermal plants and thirteen solar plants to service its unique hourly load demand. Results showed that the proposed MSA gave preference to solar photovoltaic generation over thermal generation given that environmental impact minimization is a major component of the overall objective function. The proposed MSA also scheduled thermal generators to provide the required spinning reserve capacity since solar energy is intermittent in nature. Overall, analyses show that the proposed MSA outperformed the other methods in finding the best fuel, emission, solar generation, and spinning reserve costs that best serve the energy demand of the data centre and the second test system for the 24-hour period.

Ant Colony Algorithm for Time Optimal Load Balancing Scheduling Problem Strategy

In order to optimize the task execution efficiency of FPGA high-performance network clusters, this paper proposes a time-optimal load balancing scheduling problem algorithm and initialization strategy. Taking the task completion time as the optimization direction, the load balancing scheduling problem is abstracted as solving the placement matrix problem. On this basis, a node selection strategy using critical matrix is proposed, and the algorithm design is completed based on the ant colony algorithm. Aiming at the problem of low efficiency of random initialization, this paper proposes two ant colony initialization methods, which are ranked greedy initialization strategy and polling decentralized initialization strategy. Experiments show that the algorithm and initialization strategy designed in this paper can adapt to different computing scales, and the two initialization strategies can achieve a 15%∼55% improvement in the iterative convergence times and load balancing compared to random initialization.

The split heterogeneous vehicle routing problem with three-dimensional loading constraints on a large scale

This paper introduces a complex real-world problem on a large scale that combines the split delivery heterogeneous vehicle routing problem with three-dimensional loading and considers new routing and loading constraints. The problem aims to find a set of routes with minimum transportation cost by satisfying the demand of all customers and loading constraints. We propose a column generation-based heuristic algorithm that employs three heuristic algorithms to solve the subproblem and a hybrid algorithm for loading. Computational experiments on the literature instances indicate that the proposed algorithm can produce very near solutions to the literature algorithm's solutions in significantly shorter running times. This paper introduces real-world instances with 230 to 850 customers, several times larger than the existing benchmarks. The results of the proposed algorithm for real-world instances are effective, and compared to the current situation, it leads to a reduction of 20% in the average total cost of transportation. Also, the average number of vehicles is reduced by 36%.

An adaptive backpropagation algorithm for long-term electricity load forecasting.

Artificial Neural Networks (ANNs) have been widely used to determine future demand for power in the short, medium, and long terms. However, research has identified that ANNs could cause inaccurate predictions of load when used for long-term forecasting. This inaccuracy is attributed to insufficient training data and increased accumulated errors, especially in long-term estimations. This study develops an improved ANN model with an Adaptive Backpropagation Algorithm (ABPA) for best practice in the forecasting long-term load demand of electricity. The ABPA includes proposing new forecasting formulations that adjust/adapt forecast values, so it takes into consideration the deviation between trained and future input datasets' different behaviours. The architecture of the Multi-Layer Perceptron (MLP) model, along with its traditional Backpropagation Algorithm (BPA), is used as a baseline for the proposed development. The forecasting formula is further improved by introducing adjustment factors to smooth out behavioural differences between the trained and new/future datasets. A computational study based on actual monthly electricity consumption inputs from 2011 to 2020, provided by the Iraqi Ministry of Electricity, is conducted to verify the proposed adaptive algorithm's performance. Different types of energy consumption and the electricity cut period (unsatisfied demand) factor are also considered in this study as vital factors. The developed ANN model, including its proposed ABPA, is then compared with traditional and popular prediction techniques such as regression and other advanced machine learning approaches, including Recurrent Neural Networks (RNNs), to justify its superiority amongst them. The results reveal that the most accurate long-term forecasts with the minimum Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values of (1.195.650) and (0.045), respectively, are successfully achieved by applying the proposed ABPA. It can be concluded that the proposed ABPA, including the adjustment factor, enables traditional ANN techniques to be efficiently used for long-term forecasting of electricity load demand.

Risk Factors of Tumor Patients with Peripherally Inserted Central Catheter Related Thrombosis and Nursing Intervention Analysis Mediated by Algorithm-Based Ultrasound Images

This paper aimed to explore the risk factors of peripherally inserted central catheter (PICC) related thrombosis (PICC-RT) in tumor patients mediated by ultrasound images under minimum variance (MV) algorithm and put forward the corresponding nursing intervention methods based on the risk factors. The smoothing algorithm, diagonal loading algorithm, and coherence factor algorithm were optimized via MV algorithm. The optimized algorithm was compared with other algorithms to analyze its image quality and image processing speed. Literature retrieval was conducted to analyze the risk factors of PICC-RT in tumor patients. Tumor patients who received PICC for chemotherapy in the hospital from June 2018 to December 2019 were selected. Patients who were accepted before the experiment were taken as the controls, and there were control group (89 cases) and observation group (91 cases). Exercise, average flow rate of axillary vein per unit time, and PICC-RT were compared between the two groups. The results showed that the optimized algorithm (MV-N) had better image resolution, image contrast, and calculation speed than other algorithms. Gender, body mass index (BMI), pathological type, clinical stage, disease history, fibrinogen (FIB), and use of anticoagulant drugs were risk factors for PICC-RT in tumor patients. The number of PICC-RT and complications in the observation group was notably lower in contrast to the control group ( P < 0.05 ). It indicated that a novel algorithm was successfully established, which could increase the ultrasonic image quality and computing speed, and upper limb exercise could reduce the incidence of PICC-RT and its complications in tumor patients.

Short-term Load Forecasting by Using Improved GEP and Abnormal Load Recognition

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and R 2 .

Modeling of a simplified hybrid algorithm for short-term load forecasting in a power system network

PurposeThe purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series.Design/methodology/approachShort-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity.FindingsThe proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads.Originality/valueThe effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy.

Research on Optimal Scheduling Algorithm for Interruptible Load for Clean Electric Energy

The rapid growth of electric energy demand and the power construction of the lag have led to the problem of electricity shortage. At the same time, the idea of energy conservation and emission reduction and low-carbon environmental protection is gradually deepening in people’s hearts, making it inhibited by improving the development model of the installed capacity of the fire generator set. As an important measure to respond to the demand side response, the interrupt load is an effective means of solving this problem. In this context, this paper proposes an interrupting load management method that considers the advance notification time and uses a priority order method. Solving, making the application of interrupt load more secure and reliable and economic goals.

Implementation of Underground Water for Wireless Optical Communication System using CDMA

Optical wireless communications is a powerful and cost-effective approach for high-speed wireless links that have been tightly guarded For underwater optical wireless communication, the following three optical code division multiple access (CDMA) techniques have been used. systems are associated, investigated, and presented in this paper, such as AC-biased optical CDMA (ACO-CDMA), symmetrically-SCO-OFDM (clipped optical OFDM), and unipolar CDMA (U-CDMA). Peak power constraints, light source bandwidth tag, there are so many factors to recognize, such turbulence, fading underwater signals, and channel estimation error. Advocate for a bit loading algorithm and a simplified modulation index that determines signal magnitude is being used to minimize the achievable data propagation distance. In this optimization procedure, the signal-to-noise ratio and the clipping distortion triggered by the peak power limitation are balanced (SNR). The SNR and clipping effects of the three compared CDMA techniques are represented in this paper. When the transmitted bit index is greater than the channel bandwidth, ACO-OFDM outperforms SCO- and UCDMA, according to the determined model. U-CDMA, on the other end, has a longer propagation distance but needs less transmitted power.

New Solution Algorithm for Peak Shaving and Load Leveling Using Photovoltaic/Battery Aggregation.(Dept.E)

In one of familiar and important past work in shaving the peak and leveling the load using Photovoltaic system/Battery storage (PV/BS) aggregation, the authors assumed their sizes as hypothetical numbers [1]. Moreover, a significant question of how the modified daily conventional generation curve be affected by different PV-penetration levels hadn't been answered. On other hand, this work hadn't investigated or analyzed the influence of PV/BS hardware prices and the PV-penetration level on the annual savings on using combustion turbines instead of the above aggregation. So. our paper introduces a proposed solution algorithm to answer these questions provided with a complete numerical application (or Egyptian meteorological conditions. Operating the PV/BS combination with the utility grid has been optimized subject to a set of several and economic constraints. Three design concepts are thought of and suggested differed principally in the source of charging the battery either from PV and /or utility grid. Entire quantitative analysis of the composile impacts of the economic parameters and penetration level of py-array operating at its maximum power point has been performed and discussed. The best choice or the design concept to be applied and the adequate penetration level dep ends on the value of the levelized annual savings (LAS) on using the above aggregation instead of conventional generation units. It may have a value up to 406.317 $/K W-year. This algorithm can, therefore, be considered as an effective tool in instituting accurately the modified daily conventional generation profile of twelve representative days of the year months. Moreover. It enables the designer lo determine the applicable range of Py-penetration levels and economic parameters resulting in largest savings.

Minimizing makespan and flowtime in a parallel multi-stage cellular manufacturing company

• We formulate an approach for a parallel multi-stage cellular manufacturing system. • Makespan and total flowtime found in reasonable time for large problems. • Genetic algorithm is proposed to improve the efficiency of the cell loading process. • Approach verified by shoes manufacturing case study to yield. • More effective approach offered by using multi-stage solution ways. This study proposes a 3-phase solution approach for a multi-product parallel multi-stage cellular manufacturing company. The study focuses on a case study involving a shoe manufacturing plant in which products are produced according to their due dates. The investigated manufacturing process has three stages, namely lasting cells, rotary injection molding cells, finishing-packaging cells. System performance is measured based on total flowtime and makespan. We propose a 3-phase solution approach to tackle the problem; 1) the first phase of the proposed approach allocates manpower to operations in the lasting cells and finishing-packaging cells, independently. The objective is to maximize the production rates in these cells. 2) The second phase includes cell loading to determine product families based on a similarity coefficient using mathematical modeling and genetic algorithms (GA). The proposed GA algorithm for cell loading performs mutation prior to crossover, breaking from traditional genetic algorithm flow. The performance measures flow time and makespan are considered in this phase. 3) Flow shop scheduling is then performed to determine the product sequence in each (lasting, rotary injection molding, finishing-packaging) cell group. This 3-phase solution approached is repeated with alternative manpower level allocation to lasting and finishing-packaging cells where the total manpower level remains the same.

GSvit — An open source FDTD solver for realistic nanoscale optics simulations

Surface and volume imperfections can significantly affect the performance of nanoscale or microscale devices used in photonics, optoelectronics or scientific instrumentation. In this article we present an open source software package for Finite-Difference Time-Domain electromagnetic field calculations suitable for calculations on graphics cards. Its special features include handling realistic models of imperfect nanoscale objects, such as treatment of arbitrary geometries including addition of random roughness to any geometrical object. The method is compared to conventional optical approach represented by Rayleigh-Rice theory. Practical applicability is demonstrated using a calculation of variation of field enhancement at proximity of a rough nanoscale antenna and rough particle scattering. It is shown that such approach can be namely useful in the areas where many repeated calculations are necessary, e.g. when studying how the optical response of nanoscale objects can vary when they are rough. Program summaryProgram Title: GSvitCPC Library link to program files:https://doi.org/10.17632/k424zbsxnk.1Licensing provisions: GPLv2Programming language: CNature of problem: If we want to analyse impact of random imperfections, in particular surface roughness, on optical response of nanoscale and microscale objects, we need to run many calculations with different random realisations. GSvit is a general electromagnetic field solver optimized for running such calculations, via fast computing on graphics cards and algorithms for loading of arbitrary data and modification of their geometry to construct randomly rough surfaces and interfaces.Solution method: Finite-Difference Time-Domain method implemented on computer processor and on graphics card, with advanced pre-processing in order to add realistic roughness to 3D objects.Additional comments including restrictions and unusual features: The current version of software, including the documentation and downloadable examples can be found on http://gsvit.net/.

Discrete bit-loading for OFDM systems under BER-constraint and imperfect CSI

ABSTRACT In this paper, a low complexity discrete bit loading is proposed for OFDM systems over a fading channel and imperfect channel state information (CSI). The available CSI at the transmitter can be partially known due to imperfection in channel estimation, interferences or the Doppler spread. These imperfections increase the bit error rate (BER) for each subchannel. Uniform power allocation and finite granularity for constellation size are used to maximise spectral efficiency subject to a target average BER. The proposed bit loading algorithm, starts by loading all the subcarriers with an initial bit vector obtained using the available CSI and under constraint of the target BER and the maximum number of bits allowed. Then, we introduce the errors on the CSI in the new formula of the average BER calculated initially by using all the subcarriers indexes of the initial bit vector. Iteratively, only the subcarriers, which violate the constraint on the average BER, are used for the updating; they are unloaded until reaching the final bit vector. Therefore, the set of concerned sub-carriers decreases after each iteration, this results in a significant reduction in the computational complexity.

A method for calculating loads on roof support in mines

The author proposes an approach to the stress–strain analysis of mine support with regard to all displacements of the stope boundary until it makes contact with the support using the stiffness matrix of intact rock mass. Modeling of the void formation and of the joint deformation of the support and adjacent rock mass uses iterative procedures from the methods of initial stresses and initial strains. The support load algorithm and software package are developed.

Enhanced active VM load balancing algorithm using fuzzy logic and K-means clustering

With the rapid development of data and IT technology, cloud computing is gaining more and more attention, and many users are attracted to this paradigm because of the reduction in cost and the dynamic allocation of resources. Load balancing is one of the main challenges in cloud computing system. It redistributes workloads across computing nodes within cloud to minimize computation time, and to improve the use of resources. This paper proposes an enhanced ‘Active VM load balancing algorithm’ based on fuzzy logic and k-means clustering to reduce the data center transfer cost, the total virtual machine cost, the data center processing time and the response time. The proposed method is realized using Java and CloudAnalyst Simulator. Besides, we have compared the proposed algorithm with other task scheduling approaches such as Round Robin algorithm, Throttled algorithm, Equally Spread Current Execution Load algorithm, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). As a result, the proposed algorithm performs better in terms of service rate and response time.

Adaptive Coding and Bit-Power Loading Algorithms for Underwater Acoustic Transmissions

Underwater acoustic channel (UAC) is featured as fast time-varying characteristic, and challenges the transmission designs. To countermine the time variation effect, we propose an adaptive design for orthogonal frequency division multiplexing (OFDM) transmission systems by utilizing the long-term stability of the second-order statistics of the channel state information (CSI). We derive the analytical expression of signal-to-interference-plus-noise-ratio (SINR) at each subcarrier to reach the target error performance based on the statistical information of the CSI. Thereafter, a new adaptive coding and bit-power loading algorithm with low computational complexity is proposed to pursuit the highest achievable bit rate with fixed error rate. The validity of SINR calculations and the effectiveness of the proposed adaptive algorithm are demonstrated under various conditions, wherein both simulated and measured channels have been tested.

A Dynamic Load Balancing Strategy Based on Improved Ant Colony Algorithm

Load balancing technique plays a fundamental role in distribution, while facing numerous challenges. It is difficult for ordinary load algorithms to cope with complex scenarios, and it is likely that uneven load will lead to downtime of a single server, thus causing an avalanche effect and eventually breaking down the whole cluster. This paper proposes a load balancing strategy based on improved ant colony algorithm, considers the influence of task length and processing efficiency in the algorithm based on the original ant colony algorithm, and takes the load rate of nodes as an important index for ants to select path. In addition, the load coefficient includes CPU, memory, IO, and network bandwidth into the calculation range, which makes the obtained server load condition more accurate. Finally, test data of different algorithms are obtained through experiments, and the advantages and disadvantages of this load balancing strategy are verified after horizontal comparison.

Integrated Model for Timetabling and Circulation Planning on an Urban Rail Transit Line: a Coupled Network-Based Flow Formulation

The recent development of advanced communication and data collection technologies enables a wide range of possibilities for systematic planning, operation, and control of urban rail transit systems in many megacities. While traditional methods consider tactical transit timetabling and operational circulation planning as two independent stages, this study aims to propose an optimization model and solution scheme to fully integrate these two supply-side stages in response to passenger demand dynamics. We first construct a new formulation through two coupled space-time network representations, namely, the transit space-time network and passenger space-time network, with many embedded constraints. In detail, the transit space-time network covers constraints involving train fleet size, deadheading and holding operations, headway requirements, and running and dwell times; meanwhile, the passenger space-time network is used to represent passenger traveling processes and the resulting trajectories. A coupled network-based flow optimization model is accordingly established to minimize passenger total travel time with a fixed train fleet size. To handle large-scale problem instances, we first adopt a constraint splitting technique to form two subsets of Lagrangian multipliers corresponding to individual passenger decision constraints and train capacity constraints. A dual decomposition scheme is then developed to iteratively coordinate the adjustment of Lagrangian multipliers and solve the related two subproblems. Specifically, the passenger subproblem is solved by a passenger loading algorithm, and the train subproblem is decomposed and solved by the alternating direction method of multipliers. The effectiveness of the proposed model and solution approach is evaluated on a real-world case study based on the Batong Line in the Beijing subway network.

A novel adaptive QAM coder /decoder for bit and power loading in IM/DD OFDM-PON transmission system

For fully utilizing the system frequency response of optical intensity-modulation and direct-detection (IM/DD) passive optical network (PON) transmission system, this paper demonstrates a novel adaptive quadrature amplitude modulation (QAM) encoder /decoder for bit and power loading based on adaptively modulated orthogonal frequency division multiplexing (AM-OFDM). The applied adaptive QAM encoder/decoder supports all the M-QAM formats on each sub-carrier instead by ranging the order of modulation format on different sub-carriers. Simulation results show that the adaptive QAM coder/decoder can be used for variable transmission channel with different transmission distances between optical line terminal (OLT) and optical network units (ONUs). Meanwhile, the adaptive loading algorithms minimizes the bit error rate (BER) for a given bit rate. In addition, compared to the fixed 16QAM scheme, the receiver sensitivity performance of the adaptive QAM scheme is improved by 5.2 dB for 30 km standard single-mode fibre (SSMF) transmission at the -8.12 dBm received optical power (ROP).