Output Loss Research Articles

Comprehensive analysis of line losses and valuation of energy savings in optimized photovoltaic array subjected to partial shadings

In this manuscript, an attempt is made to reconfigure the conventional total cross-tied (TCT) array through a novel optimized SuDoKu (OPSDK) static reconfiguration technique. This approach helps to reduce the line losses occurring due to increased wire length and simultaneously diminishes the adverse effects of accumulated shadings in close vicinity by diffusing it across the photovoltaic array (PVA). The MATLAB/Simulink platform is used to study the variations in the performance of various existing topologies and the proposed OPSDK PVA model under various shading scenarios such as bottom-right corner, bottom-left corner, top-right corner, top-left corner and drifted-locus centre. The performance is monitored with regard to the specific characteristics viz., % fill-factor, mismatch power losses, % power loss, % efficiency, global maximum power point, maximum voltage, maximum current, local maximum power points (LMPPs), number of LMPPs, misleading power loss, current loss, voltage loss, % execution ratio, % power improvement, % thermal voltage, % power enhancement ratio, line loss and annual energy saving. Theresults show that the proposed OPSDKmethod generates 28.12852 kWh energy units per day, which is maximum when compared to other five topologies viz. SuDoKu (SDK), futoshiki SuDoKu (FSDK), skyscraper SuDoKu (SKYSDK), modified SuDoKu (MSDK), ancient chinese magic square (ACMS). Consequently, the annual income generatedby [9×9] SDK, FSDK, SKYSDK, MSDK, ACMS and OPSDK PVAs is ₹ 150,839.54, ₹ 141,514.52, ₹150,108.08, ₹ 150,751.94, ₹ 150,249.33 and ₹154,003.72, respectively, signifying that OPSDK PVA produces highest income. The extensive analysis signifies that the proposed OPSDK topology succeeds in reducing line losses by 25.62 %, 0.00 %, 37.98 %, 33.43 % and 35.49 % in comparison to SDK, FSDK, SKYSDK, MSDK and ACMS, respectively. The mismatch power losses in power output are also reduced by 643.1 W as compared to the other configurations. The OPSDK technique, thus, proves to be one of the most efficient, effective, optimised and sustainable PVA reconfiguration technique with good power savings and greater economic returns/viability.

Performance analysis of various deep learning models for detecting rice diseases

<p>A major portion of the world’s population relies on rice as a staple diet, hence rice is essential to maintaining food security worldwide. Unfortunately, rice crops are susceptible to a number of illnesses that, if detected and treated promptly, can result in significant output losses. Expert visual inspection is a time-consuming and arbitrary part of the conventional procedures for diagnosing diseases in rice. An effective method for automated illness diagnosis in agriculture has evolved in recent years: deep learning, a branch of artificial intelligence. The objective of this research is to compare AlexNet, DCNN, MobileNet, GoogleNet, VGG16, ResNet50 and Xception, these are various deep learning models in order to choose the one that would produce the highest levels of accuracy, precision recall, specificity, and F1-score for detecting rice diseases. In this study we train the model for nine different types of rice diseases named as Rice Blast (Pyricularia oryzae), Rice Sheath Blight (Rhizoctonia Solani), Bacterial Leaf Blight (Xanthomonas oryzae pv. oryzae), Tungro Disease, Rice Grassy Stunt Virus (RGSV), Rice Yellow Mottle Virus (RYMV), Bakanae Disease (Fusarium moniliforme), Brown Spot (Cochliobolus miyabeanus) and Rice Tungro Bacilliform Virus (RTBV) with 30,000 images. For this we used the secondary dataset for analyzing the performance of models. We trained the model for both normalized and non-normalized dataset. After comparing the various models we get the better result from ResNet50 model with accuracy of 97.50%.</p>

What types of business environment fosters the emergence of more specialized and sophisticated “little giant” enterprises?—An empirical study based on the TOE framework and configuration adaptation theory

AbstractNurturing specialized and sophisticated enterprises is a vital strategic initiative to mitigate the dependency on crucial technologies and to promote the competitiveness of Chinese manufacturing industry. Basing on a technology, organization, and environment (TOE) framework, combined with geographic concentration, qualitative comparative analysis (QCA), and data envelopment analysis (DEA) methods, this article analyzes the conducive business environment for the growth of specialized and sophisticated “little giant” enterprises (LGEs) from configurational perspective. It takes the quantity and density of LGEs in different regions of China as the consequence event. The findings show that: (1) the spatial distribution of LGEs is highly uneven with a geographic concentration index (CR5) of 0.436 and a Herfindahl–Hirschman Index of 0.0599. (2) The presence of numerous technology‐oriented SMEs and a “favorable” business environment are not necessarily the regions where a larger number of LGEs are fostered. Indeed, the harmonious interplay of TOE is more important for achieving optimal outcomes. The differential configurations contributing to the regional output variations of LGEs can be classified with three patterns: technology‐organization‐environment synergistic promotion type (H1), innovation stimulation through effective financial support type (H2a), and compensatory type for inadequate innovation resources (H2b). While these configurations may lead to similar outcomes, they do not yield the same effectiveness. (3) The DEA reveals a tendency for “over‐optimization” in improving regional business environment under the TOE framework, indicating the presence of “input redundancy” and corresponding “output loss” issues in TOE factors. Local governments must adhere to the “barrel principle” and optimize the business environment that aligns with development of LGEs through the concept of dynamic adaptation. Adopting an organizational mindset of “stringing beads into chains” can help construct regional industrial chains with distinctive features.

Designing a Smart Agri-Crop Framework on Cotton Production using ABO Optimized Vision Transformer Model

Due to its widespread cultivation and large yields by most farmers, cotton is another vital cash crop. However, a number of illnesses lower the quantity and quality of cotton harvests, which causes a large loss in output. Early diagnosis detection of these illnesses is essential. This study employs a thorough methodology to solve the crucial job of cotton leaf disease identification by utilising the "Cotton-Leaf-Infection" dataset. Preprocessing is the first step, in which noise is removed from the dataset using a Prewitt filter, which improves the signal-to-noise ratio. Next, a state-of-the-art process for image classification errands called Vision Transformer (ViT) model is used to carry out the disease categorization. Additionally, the study presents the African Buffalo Optimisation (ABO) method, which optimises weight during the classification procedure. The African buffalo's cooperative behaviour served as the model's inspiration for the ABO algorithm, which is remarkably effective at optimising the model's parameters. By integrating ABO, the problems caused by the dynamic character of real-world agricultural datasets are addressed and improved model resilience and generalisation are facilitated. The suggested ViT-based categorization model shows remarkable effectiveness, with a remarkable 99.3% accuracy rate. This performance is higher than current models.

Inflationary oil shocks, fiscal policy, and debt dynamics: New evidence from oil-importing OECD economies

Appropriate fiscal and monetary policies are essential for mitigating the surging inflation caused by post-Covid 19 commodity price shocks, whilst minimizing real output losses. Considerable attention, however, has focused on the implications for monetary policy but somewhat neglected the fiscal dimension. By estimating Proxy-VARs we study the impact of inflationary oil supply and demand shocks on the dynamics of the fiscal balance and public debt in seven oil-importing OECD economies and the Eurozone bloc. The results indicate that fiscal balances actually increase in the medium-term following an inflationary oil shock, and public debt-to-GDP ratios fall. A key implication is that policy makers have more fiscal space than typically assumed, and will run the risk of an excessively contractionary macroeconomic policy stance if they do not account for this increase in the fiscal balance following inflationary oil shocks.

Examining the U-shaped relationship between non-agricultural sources water pollution and the urban-rural income gap.

Determining how the economy and society interact with the environment of water quality is essential to determining the financial impact of green development. Based on China's provincial panel data from 2010 to 2021, this research considers non-agricultural sources of water pollution (NASWP) as a negative factor of production, investigates its influence on the urban-rural divide, and explains the mechanism of action. The empirical results show that there is a significant correlation between NASWP and the urban-rural gap, with a "U-shaped" relationship between the two. Water pollution first reduces and then increases the urban-rural income gap, and the results are robust after considering endogeneity. Mechanistic research demonstrates that NASWP cause a loss in food output, which in combination with changes in food prices and food subsidy programs impacts the incomes of rural dwellers, thereby having an influence on the urban-rural income gap. Using the threshold effect model, it is discovered that under the combined influence of agricultural mechanization and food subsidy policy, the relationship between NASWP and urban-rural income divide exhibits an U-shape in areas with high agricultural mechanization and an "inverted U" shape in areas with low agricultural mechanization.

Calculation and analysis of the efficiency of resource allocation for technological innovation in China.

The efficiency of resource allocation in technological innovation is a critical factor influencing the output level of technological innovation. By expanding and optimizing the Hsieh & Klenow (2009) framework for analyzing the efficiency of resource allocation and relaxing the assumption of constant returns to scale, this study utilizes sample data from Chinese listed companies from 2007 to 2019 to measure and analyze the resource allocation efficiency level in China's technological innovation. The findings indicate that in the process of technological innovation, companies face heterogeneous resource usage costs, leading to a deviation from the optimal resource allocation state, with evident issues of resource misallocation. The loss of efficiency in technological innovation output due to resource misallocation is significant, and addressing this issue can substantially enhance the level of technological innovation output. The misallocation of research and development capital resources is more severe than that of research and development personnel, resulting in greater efficiency losses in technological innovation output. Government subsidies are identified as a significant factor affecting resource allocation in technological innovation. Addressing the issue of resource misallocation, accelerating the market-oriented reforms of technological innovation resource allocation, and optimizing the government subsidy screening mechanism are crucial for improving the efficiency of resource allocation in technological innovation.

Early Disease Detection in Plants using CNN

The Indian economy benefits from early disease detection in plant leaves. According to reports, 10-30% of crops suffer harm from diseases that are not discovered during the curing process. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have paved the way for transformative solutions in disease identification for agriculture. This study addresses the critical issue of early disease detection by harnessing the power of deep learning models, specifically CNNs. Different leaf disease detection technologies are used for different crops. The pre-trained deep learning model is used in this study to identify and categorize leaf diseases. A dataset of tomato, potato, and bell pepper leaf pictures from the plant village repository was employed for the current investigation. The developed model can detect 12 plant diseases in normal leaf tissue. The quantitative assessment of our CNN-based technique reveals an impressive accuracy rate of 86.21%. This notable accuracy underscores the efficacy of our approach in the challenging domain of plant disease detection. Our findings show potential for the larger agricultural environment beyond the immediate quantitative advantages. This technical advance not only paves the way for improved crop output and lower losses, but also brings in a new era of data-driven sustainability in agriculture. The bottom line is that our research "offers a tangible pathway for leveraging AI-powered solutions to address the long-standing challenges of plant disease detection, thereby significantly contributing to the well-being of farmers and the sustenance of the Indian agricultural sector.

Deep Learning based Mango Leaf Disease Detection for Classifying and Evaluating Mango Leaf Diseases

Mango is one of the important commercial crop in the world. It provides nutritional and financial support to human life. Different diseases of leaves impact the health of the mango crops. The early and proper pest control measurement can prevent large output losses. We propose an automated inspection and classification of disease-affected mango leaves that uses Deep Learning (DL) model. Our DL model-empowered Convolutional Neural Network (CNN) architecture is trained with an extensive image dataset of mango leaves portraying a variety of disease indications at both low and high-resolution images. The objective is to be able to identify accurately the disease type on mango leaves including Bacterial Canker, Powdery mildew, Anthracnose, Gall midge, and Sooty mould. Crops can develop gradual immunity with reasonable pest control and can purposively shaped them against constantly evolving environment. The proposed system will be effective and it will definitely prove a facile system to be used as a key component of a novel precision agriculture system as will be presented in our future work. The performance of the proposed system is augmented through the utilization of transfer learning techniques and pre-trained models, including VGG-16, MobileNet, Googlenet, YoloV8, and EfficientNet. These Deep Learning models not only offer an accurate and efficient approach for classifying diseases in mango leaves but also provide valuable insights into the severity of the identified diseases. Utilizing this information to support farmers and agricultural professionals in making informed decisions pertaining to disease management and treatment strategies can significantly contribute to the sustainable growth of mango crops. The development and implementation of such automated technologies have the potential to revolutionize the monitoring of mango crop health, enabling early disease detection and enhancing crop yields.

Inflation targeting, exchange rate and output gap in Central and Eastern European countries

This paper aims at empirical assessment of monetary policy relationships in Central and Eastern European countries (Czechia, Hungary, Poland, Romania), which practise the monetary regime of inflation targeting. Using quarterly data for the period of 2004-2019, the authors examined the relationship between the output gap, nominal and real exchange rate misalignment, consumer price inflation and the central bank policy rate, using the panel vector autoregression (PVAR) model. According to estimates, the central banks of the CEE-4 countries react with an increase of their policy rate to the output gap and inflation, as implied by the Taylor rule. At the same time, a monetary reaction to nominal (real) exchange rate misalignment is lost in the estimates for the post-crisis period of 2010-2019. An increase of the central bank rates does not seem to have unfavourable output effects, while the nominal exchange rate is likely to depreciate with three to five quarter lags. However, there is evidence of the price puzzle when monetary tightening is followed by a counterintuitive increase in the inflation rate. In full accordance with the New Keynesian framework, inflation depends on the output gap, while being expansionary in the real sector. Evidence of the exchange rate pass-through (ERPT) to inflation is mixed. This article provides insights into the average monetary policy reaction function and its macroeconomic effects in the CEE-4 countries, with the value added in that following the Taylor rule does not guarantee effectiveness in tackling inflation in the presence of the price puzzle. Based on the estimated exchange rate effects on the output gap and inflation, deliberate currency appreciation resulting from the central bank foreign exchange interventions and/or fiscal austerity measures can be helpful for such a disinflation that minimises output losses.

Fossil energy use and carbon emissions: An easy-to-implement technical policy experiment

<p>Macroeconomic research on carbon policy mostly revolves around carbon pricing mechanisms such as carbon taxes, cap-and-trade-schemes, and a mix of them. Despite their relevance, however, carbon pricing is not the only policy available to mitigate carbon emissions. A richer and diversified arsenal of carbon policies may prove more effective in addressing carbon mitigation across different social, economic and geographic contexts. We proposed a policy experiment, which took the form of a <italic>technical tax</italic> on fossil energy use aiming at stabilizing carbon dioxide emissions. The <italic>technical tax</italic> responded to variations in all elements that are supposed to alter climatic phenomena, that is, domestic carbon emissions, domestic fossil energy use, and the industrial stock of atmospheric carbon dioxide, to which all world economies contribute. The macro-effects of the <italic>technical tax</italic> were simulated using an off-the-shelf Real Business Cycles (RBC) model targeting the economy of the United States and featuring a climatic block. The economy was perturbed by a technology shock and an energy-price shock. Special care was devoted to the processes of validation and calibration. The tax was responsive to the business cycle and showed positive aspects. When a technology shock hit the economy, it curbed carbon emissions with minor costs in terms of potential output losses. It also protected the economy from an increase in energy prices, mitigating the fall in output despite the drop in fossil energy use. Last but not least, the tax effectively stabilized carbon dioxide emissions by reducing their variance.</p>

Newly Evolved Different Fungicide Combinations against Paddy Sheath Blight under Field Conditions in West Bengal

Rice (Oryza sativa L.) is a vital global food crop, consumed by over half the world’s population. In India, it covers 43.5 M ha, yielding 112.9 Mt at a productivity of 2.56 t ha-1. In West Bengal, it spans 5.15 M ha, producing 15.09 Mt with a high productivity of 2933 kg ha-1. Rice sheath blight, caused by Rhizoctonia solani (also known as Thanatephorus cucumeris), is a highly destructive disease that has led to significant output losses in the last two decades. Reports suggest that production losses due to this disease can range from 5.2% to 50%, depending on factors like environmental conditions, crop growth stages, management practices, and rice cultivars used. This disease is called “sheath blight” because it initially infects the leaf sheath. To combat this disease, various fungicide compounds with diverse modes of action are available on the market. The research aimed to identify the most effective and cost-efficient fungicide combination for managing rice sheath blight. The experiment evaluated the efficacy and economics of Mancozeb 52.6% + Hexaconazole 2.4%, Hexaconazole 5% EC, and Hexaconazole 4% + Zineb 68%, as well as Mancozeb 75% WP under field conditions. The results showed that Mancozeb 52.6% + Hexaconazole 2.4% achieved the highest disease reduction over the control (46.10%), followed by Hexaconazole 5% EC (42.30%).

Assessing impacts on rice production and quantifying economic cost losses with PM2.5–based damage – A case of the Long Xuyen Quadrangle, Vietnam Mekong Delta

Control measures of PM2.5 pollution mitigation are identified as an important strategy for food security. This is remarkably crucial with Long Xuyen Quadrangle (LXQ), which exports more than 50% of Vietnams rice. PM2.5 pollution impacts on rice production need to be assessed to determine the extent of effects on economic losses. The given objectives of this study are to build a research framework that combines models and data to quantify the economic damage to rice seasons affected by PM2.5 pollution, taking LXQ as a research case study. The models used include quantitative models of output loss due to loss of productivity, productivity function, and quantification of economic loss model to assess economic loss due to loss of productivity caused by PM2.5, based on damage to rice plants. The data used included: (i) 24-hour average PM2.5 concentration data in the environment to represent the process of estimating negative impacts on rice yield, (ii) formal characteristics of rice crops, (iii) rice area/total agricultural land area, (iv) rice production level achieved by each crop, and (v) minimum support prices for rice. The total loss of rice yield in the LXQ in 2018 was estimated at 15,729 tons/ha. The results associated with calculated damage levels into economic values showed that the total losses due to the reduction of rice production in 2018 in LXQ were at about 2,728 billion VND, which was equivalent to approximately 121 million USD, or 1.47% of the total GRDP of LXQ in 2018. The obtained results will be the basis for further development, contributing to the building of a dataset for the management of the air environment as well as effective agricultural farming solutions for the LXQ area.

Etiology and management of citrus Melanose disease in Pakistan: A review

Diaporthe species are important pathogens of many hosts, including economically important crops. They cause severe melanose disease, stem-end rot of fruits, and gummosis of perennial plants worldwide. Among the Diaporthe species, Diaporthe citri is responsible for causing melanose disease. Melanose disease harms fruit, leaves, twigs, branches, and in certain cases, the main stem of trees of any age. The melanose infection can affect practically all citrus cultivars. Citrus melanose disease is a persistent and incurable disease leading to a massive loss of output in the citrus industry. Due to the lack of resistant cultivars, the failure of chemical treatments, and the dangers to environmental health, large-scale research is being conducted to develop a long-term remedy. Recently, biological control agents (BCAs) have been used as an alternative to fungicides to manage citrus melanose disease. In this review, we highlighted some significant aspects of melanose disease (Diaporthe citri) in citrus, such as its history, morphology, disease cycle, pathogenicity symptoms, and impact on the economy of the citrus industry. The current literature assists the researchers in averting melanose disease in citrus, developing genetically resistant cultivars, and ecofriendly management against Diaporthe citri. Keywords: Diaporthe citri, Melanose, Symptoms, Transmission, Disease cycle, Control management

Mapping water scarcity risk in China with the consideration of spatially heterogeneous environmental flow requirement

Water is an indispensable raw material for industrial production across various economic activities. Extensive research indicates that local water scarcity may cause severe economic output losses, posing risking not only to the affected regions (referred to as local water scarcity risk, LWSR) but also to distant regions through trade networks (known as virtual water scarcity risk, VWSR). To assess water scarcity risk (WSR), a key metric used is the environmental flow requirement (EFR), which exhibits considerable spatial heterogeneity. However, previous studies have primarily employed a constant EFR value (approximately 80%), limiting the reliability of the WSR findings. To address this limitation, we propose a comprehensive framework that incorporates spatially heterogeneous EFR. Our analysis reveals that in 2017, the WSR in China amounted to over 3.9 trillion US dollars, consisting of 1.6 trillion dollars attributed to LWSR and 2.3 trillion dollars to VWSR. Through examination at provincial, sectoral, and trade-link levels, we identify specific WSR hotspots and present relevant policy implications. Additionally, by comparing our results with those obtained using the 80% EFR criterion, we uncover significant differences. These findings emphasize the importance of considering spatially heterogeneous EFR and contribute to more accurate evaluations of WSR.

Unveiling the unequal variation of regional carbon risk under inter-provincial trade in China

Development and emissions reduction are key conflicts that China needs to balance under the “dual‑carbon” goal. The risk of potential output loss due to limiting economic growth by carbon emission stress will be further propagated through the trading system. This study evaluated the level of regional carbon risk by constructing a carbon risk index, then quantified the transfer of carbon risk under interprovincial trade based on the multiregional input-output model, and ultimately revealed the unequal changes of carbon risk between provinces. The results show that the level of carbon risk was consistently higher in Northwest China (Inner Mongolia, Ningxia, Xinjiang) and North China (Shandong, Hebei) during 2007–2017. There was an inequality between inter-provincial embodied carbon flow and carbon risk transfer, which made the sum of net carbon risk imports across provinces positive rather than zero. In addition, the risk-transfer value matrix identified helps to underpin mitigation actions across provinces, and the inter-provincial trade with a lower risk-transfer value needs to be improved. Furthermore, the continued increase of carbon risk in some less developed regions, such as Inner Mongolia, was found to be difficult to reverse, making it important to reduce emissions from the production side in order to minimize risk.

Gauging Effects of Hypothetical Extraction Method on Transport Linkages in India

Transport infrastructure is imperative for sectoral growth and economic progress. To gauge the impact of reduced contributions by the Railway and Other Transport sectors on other sectors in India, we use the Hypothetical Extraction (HE) based methodology to analyse the impact of such changes on outcomes of different sectors. Five Input-Output Transaction Tables (IOTT), between the years 1993-2014, are aggregated into 22 sectors. Methodologically, sector pair-wise correlations and sectoral groupings are identified in the transport sector. Induced elimination of Railway and Other Transport sector contributions are used to capture sectoral dependencies. Changing patterns in ‘Key’ sector compositions are further identified. Manufacturing, Mining and Quarrying, Construction, Storage and Warehousing and Electricity appear significantly correlated with Transport. Extraction causes sizeable output loss (around 60 perent) for the Manufacturing sector. ‘Self-extraction’ results in declining loss of output for transport sectors, whereas, Electricity and Storage and Warehousing remain ‘Key sectors’ in the post-extraction economy. An important implication emerging from our analysis is that dynamic relational dependencies across other sectors must be considered for future investments in transport infrastructure.

Optimized design and application performance analysis of heat recovery hybrid system for radioisotope thermophotovoltaic based on thermoelectric heat dissipation

Radioisotope thermophotovoltaic (RTPV) generators face a fundamental challenge, which is the significant output loss and energy wastage caused by the substantial thermal deposition resulting from the arrival of infrared radiation from the heat source at the energy conversion unit. This contradiction has gradually become a key limiting factor for its further development in recent years. In this work, a novel hybrid system based on the thermophotovoltaic-thermoelectric (TPV-TE) effect is proposed, in which the Bi2Te3-based I/W-type thermoelectric leg plays both heat dissipation and heat recovery for power supply. The heat recovery thermoelectric legs were prepared using a cold pressing mould and sintering method, and their Seebeck effect was used to convert the unwanted heat recover from InGaAs thermophotovoltaic cells into electrical power. The design preparation scheme of the heat recovery hybrid module and its potential application performance at 700–1000 K heat source temperature were investigated through experimental tests and simulations. The W-type TPV-TE heat recovery hybrid system exhibits good gain of 12.9% at 1000 K, and the output power can be increased by additional 21.6% in series mode. Simulations for environmental applications show better heat recovery in deep space than on Earth, with an output gain of 63.7% and an energy conversion efficiency of 11.23%. The heat recovery design enhances the competitiveness of the RTPV and provides new ideas for optimizing other heat recovery applications for thermal energy conversion systems.

Civil Conflicts and Exchange Rate Misalignment: Evidence from MENA and Arab League Members

Abstract For more than a decade, civil conflicts intensity has been high in the Middle East and North Africa, yet the monetary and financial impacts of such episodes have received relatively little attention. Using macroeconomic and conflict panel data for Arab League members, Iran and Turkey during the period 1970–2018, this paper constructs a country-specific real exchange rate misalignment index and adopts an instrumental variable approach to show that civil conflicts lead to real exchange rate overvaluations in the region: a 1 unit increase in civil conflict intensity leads to a 0.24 unit increase in the RER misalignment index. The results suggest that upward pressures on the real exchange rate, such as inflation resulting from physical capital and output loss, dominate. Economic policy during post-conflict transitions should mitigate this effect by incorporating a strategy to realign the currency.

UNet-Based Framework for Predicting the Waveform of Laser Pulses of the Front-End System in a Current High-Power Laser Facility

Performing data mining on large waveform datasets of a high-power laser facility is an important way to achieve precise regulation of a device. However, there are currently issues with missing values, noise, and inconsistency in this database of measuring pulse waveform in a current high-power laser facility. In this paper, a UNet of a series residual module is presented to predict the pulse waveform of a front-end chained system in a current high-power laser facility. The designed network is trained on grouped sequences formed by experimentally measuring pulse waveforms of a high-power laser facility. The strategies of relay output and relay loss are employed in training in order to enable the network to predict two kinds of pulse waveforms simultaneously. The trained network achieved an RMSE of 3.38% on the testing set of measuring pulse waveform at a frequency of 1 Hz, and an RMSE of 0.84% on the testing set of setting the voltage of the Arbitrary Waveform Generator (AWG). These results indicate that this method can accurately fill in paired missing values in the waveform database of a high-power laser facility. The main advantage of this method is that it can quickly couple operational parameters for prediction, and this method can be applied to predicting laser performance, cleaning one-dimensional sequences, and maintaining a waveform database.