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1
Taghizadeh-Hesary, Farhad, Naoyuki Yoshino, and Yugo Inagaki. "Empirical analysis of factors influencing the price of solar modules." International Journal of Energy Sector Management 13, no. 1 (2019): 77–97. http://dx.doi.org/10.1108/ijesm-05-2018-0005.
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Purpose One of the key drivers behind the recent growth in the global solar energy market is the decline in solar module prices. Many empirical analyses have been carried out to identify the mechanism behind this price reduction. However, studies on the price reduction mechanism of solar modules over the years have focused purely on the technological aspect of manufacturing. The purpose of this study is to consider the influence of economic and monetary factors such as the interest rate and exchange rate on solar module pricing in addition to other factors that considered in earlier studies including technology, wage rate and other energy prices. Design/methodology/approach In this paper, an oligopolistic model and econometric method are used to determine the economic factors that have an influence on solar module prices. The paper constructs a solar module pricing model and conducts a fully modified ordinary least squares analysis to estimate the influence of each factor. Analysis is conducted for the top five solar module producing countries in the world from 1997 to 2015. The five countries are the People’s Republic of China, Germany, Japan, the Republic of Korea and the USA. Findings Empirical analysis provides several findings concerning the solar module pricing mechanism. These vary for each country. However, generally the interest rate has a positive correlation with solar module prices, while the exchange rate, knowledge stock and oil price have a negative correlation with solar module prices. Practical implications First, the government must expand channels for renewable energy funding. As renewable industries are high-tech, the influence that capital cost has on technology price is significant. Government efforts to provide industries with low-interest finance will accelerate renewable business. There have been many attempts to lower interest rates for renewable energy technology to accelerate growth in the green technology market. Second, the government must expand research and development (R&D) expenditures focused on renewable energy technology. The technological advancements acquired through R&D enhance module performance efficiency, thereby reducing costs. Therefore, government policies aimed at increasing targeted R&D expenditure will be an effective means of expanding the installation of renewable energies. Originality/value Studies on the price reduction mechanism of solar modules over the years have focused purely on the technological aspect of the manufacturing. This is the first research to bring economic, monetary and technological factors of solar module pricing together.
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Mittag, Max, Tim Straube, and Christian Reichel. "Analysis of transport costs structures of solar modules: international versus domestic scenarios." EPJ Photovoltaics 15 (2024): 40. http://dx.doi.org/10.1051/epjpv/2024036.
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This study investigates the cost structure associated with transporting photovoltaic (PV) modules, comparing scenarios of international transport from China to Germany, a European manufacturing, and domestic transport within Germany. Utilizing a geometric model to calculate container utilization and transport logistics, we analyze the impact of module design, efficiency, and transportation routes on overall costs. The transport cost model considers module dimensions, container specifications, loading limits, transport modes, costs, packaging materials, and pallet prices. We apply this model to various module types, including M10, G12, and M10R wafer-based cells. Transport costs from China to Germany make up a significant part of the total PV module cost (14.7%–15.8%). In contrast, for German module manufacturing, the transport cost share is well below 2% and European manufacturing adds less than 3%. Transport costs have shown high volatility in the recent decade, and container prices are currently higher than prior to the Corona crisis. Disruptions in global logistics chains − such as shipping route blockages or spikes in container prices − can significantly impact cost structures. Transport costs for PV modules have quadrupled during Corona. We estimate that a transport cost share of ∼10% will remain relevant for the future. Higher module efficiencies lower specific transport costs (€/Wp). An increase of 1%abs leads to a transport cost reduction of 4.2%rel. Sensitivity analyses demonstrate that transport costs can account for up to 43% of the final module price in scenarios of low factory-gate module price and high shipping container costs. This study highlights the need to include transport logistics in PV module design and sourcing decisions. We recommend future LCOE assessments for solar projects include detailed transport cost evaluations for decision-making.
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Sulistyono, Dwi Setyo, Yuniaristanto Yuniaristanto, Wahyudi Sutopo, and Muhammad Hisjam. "Proposing Electric Motorcycle Adoption-Diffusion Model in Indonesia: A System Dynamics Approach." Jurnal Optimasi Sistem Industri 20, no. 2 (2021): 83–92. http://dx.doi.org/10.25077/josi.v20.n2.p83-92.2021.
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In 2019, the number of conventional vehicles in Indonesia reached 133,617,012 units, dominated by motorcycles of 112,771,136 units and passenger cars of 15,592,419 units. The high number of conventional motorcycle users can increase the number of pollutants and combustion emissions in the environment. This condition has encouraged the transition to a sustainable transport system that will be needed for decades to come, especially for the electric motorcycle to resolve the issue. This research aims to predict and estimate the market share of electric motorcycles by considering life cycle cost per kilometer. System dynamics simulations are developed to model the adoption-diffusion of electric motorcycles in Indonesia. This model has four main modules: an electric motorcycle module, a conventional motorcycle module, an economy module, and a consumer market module. This model shows a positive trend of EM market share from 2021-2030, with the market share value of EM is 0,411 in 2030. The development of retail price subsidy and electricity price scenarios is also carried out to determine the right policies to accelerate the adoption-diffusion process. Based on the scenario, the provision of retail price subsidy and a decrease in electricity price can increase the value of the EM Market Share.
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Nagarjun, Mudagal, and Roopa R. "FLIGHT TICKET PRICE PREDICTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–9. http://dx.doi.org/10.55041/ijsrem36633.
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The study uses past flight schedules, route data, and ticket prices to estimate airline ticket costs using machine learning regression. For ease of use and functionality, it has admin and user modules. Registering and logging in allows users to upload flight data for precise cost estimates. Ensuring continuous efficacy, the admin module makes data administration and system maintenance easier. The objective is to improve overall travel planning experiences by providing travelers with data- driven insights to help them make wise decisions and maximize the value of their airline ticket purchases. Keyword: Machine learning, Flight ticket Prediction, Flight fare.
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Hafeez, Ghulam, Noor Islam, Ammar Ali, Salman Ahmad, and Muhammad Usman and Khurram Saleem Alimgeer. "A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid." Processes 7, no. 8 (2019): 499. http://dx.doi.org/10.3390/pr7080499.
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With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies.
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Meng, Zixuan, Lin Hao, and Yong Tan. "Freemium Pricing in Digital Games with Virtual Currency." Information Systems Research 32, no. 2 (2021): 481–96. http://dx.doi.org/10.1287/isre.2020.0976.
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Providers of free-to-play games often gain revenue by monetizing players’ playtime, for example, through in-game advertising and by selling premium modules of the game. One emerging strategy to sell the premium module, known as the virtual selling strategy, is to set the module price based on an amount of virtual currency that players can either spend on playtime to earn or use real currency to buy. In this paper, we examine how the virtual selling strategy leads to different market outcomes than the traditional real selling strategy where players can purchase the premium module using real currency only. We focus on the differences caused by one specific feature—players can pay for the module indirectly using their playtime in the virtual selling strategy. We show that when the provider’s efficiency of monetizing players’ playtime, that is, the time revenue rate, is above a threshold, the virtual selling strategy will benefit the provider and hurt the overall consumer surplus compared with the real selling strategy, even though players in the virtual selling strategy have one additional way, that is, using their playtime, to pay for the module. We identify an undocumented overcompensation effect that causes the profit augmentation and the surplus reduction. The overcompensation effect also results in a U-shaped relationship between the equilibrium module price and the time revenue rate in the virtual selling strategy when the module only provides a small number of new gaming stages. It contradicts the traditional result from the real selling strategy that the provider shall reduce the module price when she becomes more efficient in monetizing players’ playtime.
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Yulianti, Yulianti, Endar Nirmala, Fernando David Hence Rotty, Eva Fauziah, Rizky Chaesar, and Muhammad Rafif Misbahuddin. "Pengujian Sistem ERP Apotek (GPOS - POS Modul) Menggunakan Metode Black Box dengan Teknik Error Guessing." Jurnal Teknologi Sistem Informasi dan Aplikasi 5, no. 2 (2022): 132. http://dx.doi.org/10.32493/jtsi.v5i2.17654.
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After the process of making software needs to be done a useful testing stage to ensure that the software (Software) is in accordance with the needs of the user. POS is an application used to make sales transactions. In this study, the test was conducted on four modules: the first is the inventory module, in this module there are features for the manager of goods data, stock of goods, and the price of goods, The second is the purchase module where this feature will manage every transaction of goods purchased to the supplier or manufacturer, the third is the sales module or POS, this feature is designed to manage every sales transaction to the customer or end user, and the last is the report module, where every transaction both sales and purchases and inventory data (items, stock, the price) will be recorded in detail in it. Each of these modules is arranged thus completing one cycle of business processes. Because of the complexity of the arrangement of logic forming sales transactions, the process of calculating the price of sales and calculating the value of stock goods causes losses for business people who use invalid systems. As for a case in true testing it's when we find a previously undisclosed error. The research phase begins by analyzing each function on the system to be tested, creating a testing scenario for the database structure that has been created, making documentation from the test results and the researcher concludes. From the results of the tests that have been done, it was concluded that this software is worth using.
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Fu, Xian Cheng, Lei Zhou, and Guo Jun Wen. "Ultrasonic Ranging System Based on Single Chip Microprocessor." Applied Mechanics and Materials 441 (December 2013): 360–63. http://dx.doi.org/10.4028/www.scientific.net/amm.441.360.
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Ultrasonic ranging (UR) technology has being used widely in many industries around the world. This paper aims to design and manufacture an ultrasonic distance measuring system with lower price and higher accuracy. Based on the deep study of principle of ultrasonic ranging, we designed the general plan and detailed electrical circuits for the ultrasonic distance measuring system based on single chip microprocessor of AT89S52, including the transmitter module of ultrasonic wave, receiving module of ultrasonic wave, display module etc. Then, we compiled the relative driven programs for all modules. After that, we debugged the combined system of hardware and software system. At last, the experimental results show that the cost is about 300RMB for 3 prototypes, measuring rang is 0.16~1.5m with the accuracy of 0.001mm, which means the ranging system meets the design requirements of lower price and higher accuracy.
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Petrović, Igor, Danijel Koprivanac, Ivana Heđi, and Mario Vražić. "PV System Design for Optimal Energy Production Based on Measured Data." Et2er 6, no. 1 (2024): 36–41. http://dx.doi.org/10.70077/et2er.6.1.5.
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One should always keep in mind optimizing energy and money gains while designing an integrated photovoltaic (PV) system for the client with electrical energy consumption. Any amount of electrical energy produced by PV system that is left after consumption, is given to distribution network. Given energy will always have lower price than electrical energy taken from distribution network. Therefore, PV system production size should be designed just to equal client consummation. If this is achieved, most produced energy compensates for energy that was supposed to be taken from distribution network. Consuming energy prices are always higher than receiving energy prices. In this research single consumer with PV system is designed using client courtyard, respectively to terrain possibilities. The client already contains two modules of PV system. Measured data is provided for period of 30 months as follows: classic consumer, consumer with one PV system module, and finally consumer with two PV system modules. The object of this research is to design the size of third PV system module just enough to ensure optimal performance for subject client energy compensation. It will also ensure the best prices achieved with energy given in distribution network.Ključne riječi
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Yang, Linlin, Yingluo Song, Zilin Hu, Aili Wang, Haibin Wu, and Yuji Iwahori. "Design and Implementation of Intelligent Vegetable Recognition System based on MobileNet." Embedded Selforganising Systems 9, no. 3 (2022): 82–86. http://dx.doi.org/10.14464/ess.v9i3.579.
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With the rise of food safety traceability, unmanned supermarkets and autonomous shopping, the automatic identification technology of agricultural products such as vegetables in circulation and sales has become an urgent problem. This paper designs an intelligent vegetable identification system based on MobileNet to solve intelligent identification problem of vegetable sales in supermarkets.
 The system includes main control core, visual processing module, pressure sensor, voice broadcasting module and display module. When the system detects that there are vegetables to be weighed, the visual processing module completes the classification of vegetables, broadcasts the name, unit price and total price of vegetables by voice, and displays the weight, unit price and total price by OLED. The machine vision processing module is constructed by deep separable convolution (DSC). It realizes the separation of channels and regions, so it has high computing efficiency and is more suitable for embedded devices with low memory space.
 The experimental results show that the overall recognition rate of five vegetables reaches 97.33% under three kinds of illumination. The system has the advantages of stability, intelligence and convenience.
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Casalbuoni, S., J. Baader, G. Geloni, et al. "A pre-series prototype for the superconducting undulator afterburner for the European XFEL." Journal of Physics: Conference Series 2380, no. 1 (2022): 012012. http://dx.doi.org/10.1088/1742-6596/2380/1/012012.
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Abstract We propose to develop, characterize and operate a superconducting undulator (SCU) afterburner consisting of 5 undulator modules (1 module = 2 SCU coils of 2 m length and 1 phase shifter) plus a pre-series prototype at the SASE2 hard X-ray beamline of European XFEL. This afterburner will produce an output in the order of 1010 ph/pulse at photon energies above 30 keV. The project is divided into the production of a pre-series prototype module and a small-series production of 5 modules. Central goals of this R&D activity are: the demonstration of the functionality of SCUs at an X-ray FEL, the set up of the needed infrastructure to characterize and operate SCUs, the industrialization of such undulators, and the reduction of the price per module. In this contribution, the main parameters and specifications of the pre-series prototype module are described.
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Tran, Duc Trong. "Assigning of land location and land price to land parcel using ArcGIS engine." Journal of Mining and Earth Sciences 62, no. 1 (2021): 27–34. http://dx.doi.org/10.46326/jmes.2021.62(1).04.
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Assigning a state price to each land parcel is a frequent and yet important task in the state management of land parcels. Land price is issued for each street. For each street, land price is divided according to level of location 1, 2, 3 and 4. Parcel is assigned to which location level depending on its walking distance to nearest street, and passed minimum alley’s width, etc. The task of valuing land parcels is cumbersome because the number of land parcels to be priced is huge. To alleviate this burden for government staff, a step by step processing model is developed to automatically determine the location level of a particular parcel. Using ArcGIS Engine library and VB.NET programming language, the steps in the proposed model are built into functions in a specialized module for land valuation. Experiment in assigning location level and land prices of Tam Hiep ward, Bien Hoa city, Dong Nai province shows that 91,73% of parcels are assigned the same location level as the location on the issued land location map. The experiment demonstrates the effectiveness and correctness of the proposed model in automatically determining location levels and corresponding prices of land parcels.
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Fu, Kui, and Yanbin Zhang. "Incorporating Multi-Source Market Sentiment and Price Data for Stock Price Prediction." Mathematics 12, no. 10 (2024): 1572. http://dx.doi.org/10.3390/math12101572.
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The problem of stock price prediction has been a hot research issue. Stock price is influenced by various factors at the same time, and market sentiment is one of the most critical factors. Financial texts such as news and investor comments reflect investor sentiment in the stock market and influence market movements. Previous research models have struggled to accurately mine multiple sources of market sentiment information originating from the Internet and traditional sentiment analysis models are challenging to quantify and combine indicator data from market data and multi-source sentiment data. Therefore, we propose a BERT-LLA stock price prediction model incorporating multi-source market sentiment and technical analysis. In the sentiment analysis module, we propose a semantic similarity and sector heat-based model to screen for related sectors and use fine-tuned BERT models to calculate the text sentiment index, transforming the text data into sentiment index time series data. In the technical indicator calculation module, technical indicator time series are calculated using market data. Finally, in the prediction module, we combine the sentiment index time series and technical indicator time series and employ a two-layer LSTM network prediction model with an integrated attention mechanism to predict stock close price. Our experiment results show that the BERT-LLA model can accurately capture market sentiment and has a strong practicality and forecasting ability in analyzing market sentiment and stock price prediction.
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Satish, Kuppani, and A. Rama Mohan Reddy. "Resource Allocation in Grid Computing Environment Using Genetic–Auction Based Algorithm." International Journal of Grid and High Performance Computing 10, no. 1 (2018): 1–15. http://dx.doi.org/10.4018/ijghpc.2018010101.
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The main core functionality of Grid Computing is resource allocation and scheduling. With the idea of genetic algorithms and microeconomics, it is proposed a Resource allocation method called a genetic-auction based algorithm [GAAB]. This algorithm contains two modules, auction module and genetic approach. Auction module find outs resource-trading price between resource provider and resource buyer, and the resource allocation carried out by Genetic algorithm by considering both time and cost constraints simultaneously. In this article, evaluations are made in the simulation environment and the results show the effectiveness of the proposed model.
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N., Baggyalakshmi, M. Janani Shree Priya, and Revathi R. "Online Shopping System." International Academic Journal of Science and Engineering 11, no. 1 (2024): 71–80. http://dx.doi.org/10.9756/iajse/v11i1/iajse1110.
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The main objective of this system is to make global shopping possible, were the customers buy things from anywhere in India. The customer will be displayed with the available products of the company, and the customer will be asked to select the items based on his choice and all information about the product like price, product id model will be given to the customer based on his requirements they make an order of the product. The modules are: Product Master, Customer Module, Purchase Module, Order Module, Billing Module. Daily, special and emergency deliveries are available. At any time during off-hours, 24 hours a day, seven days a week, we can provide your record. As days goes offline shopping has reduced and online shopping is increased. This Software will help you know the various products, product description, price range, discounts so that the customers can easily identify which product will suit best for them. The Web model that was used in the implementation of the On-Line Shopping Database Application is very simple each request from the webpage invokes a request, which then communicates with the Objectivity/DB to retrieve the requested data. The resulting data is passed by a request object to. The client-side HTML that displays the result to the user.
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Kobets, Vitaliy. "WEB SERVICE MANAGEMENT SYSTEM FOR PREDICTING REAL ESTATE PRICES USING MACHINE LEARNING TECHNIQUES." Computer systems and information technologies, no. 4 (December 26, 2024): 68–77. https://doi.org/10.31891/csit-2024-4-9.
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Today there are many different web services for renting real estate, but none of them provides price forecasting capabilities. There is a need to create a platform that allows users to receive accurate real estate price forecasts with minimal time expenditures. The aim of this paper is to develop the architecture of a web service for real estate price forecasting, considering various apartment characteristics. We have prepared a review and analysis of existing analogues of real estate rental web services, functional and non-functional requirements for a web service for apartment price forecasting. The high-level architecture and technical tasks for the participants of our web service were also developed and described in our research. The paper proposes the development of a web service that predicts real estate prices based on various property characteristics. The key objectives are: analyze existing real estate rental web services and identify functional gaps, particularly the lack of price prediction capabilities; establish technical requirements for a comprehensive web service that unifies tenants, landlords, and administrators to facilitate informed decision-making; utilize machine learning techniques, such as linear regression, random forest, and decision trees, to develop a price forecasting module within the web service; evaluate the performance of different machine learning models using RMSE metric. The paper presents the high-level architecture of the web service, including modules for user registration, data validation, apartment search and interaction, and price forecasting. The experimental results demonstrate that the random forest model outperforms linear regression and decision trees in predicting apartment rental prices in Kyiv. Overall, the study highlights the potential of integrating machine learning into real estate web services to enhance transparency and informed decision-making for both tenants and landlords.
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Bordoloi, Dibyahash. "Import and Export Database Management System." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 182–89. http://dx.doi.org/10.17762/msea.v70i1.2298.
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Project components include login, customer registration, add products, view products, purchase order, sales order, payment, and report in the import export management system. Import manager, customers, and suppliers must provide their user name and password to log in to the login module. Supplier will enter information about the product, including its name, category, price, selling price, and quantity, in the add products module. The user may examine product information in the view products module. Using the database's product id, customers can create purchase orders in the purchase order module. The supplier will validate the product's purchase order in the sales order module. When a client makes a payment using the payment module, the money is deducted from their account once the payment has been confirmed based on the customer's name, bank name, account number, company name, and customer ID. Import manager will provide reports for customer information, product information, purchase order, sales order, and supplier information in the report module.
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Yang, Sibo, Wendong Yang, Kai Zhang, and Yan Hao. "A Novel System Based on Selection Strategy and Ensemble Mode for Non-Ferrous Metal Futures Market Management." Systems 11, no. 2 (2023): 55. http://dx.doi.org/10.3390/systems11020055.
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Non-ferrous metals, as one of the representative commodities with large international circulation, are of great significance to social and economic development. The time series of its prices are highly volatile and nonlinear, which makes metal price forecasting still a tough and challenging task. However, the existing research focus on the application of the individual advanced model, neglecting the in-depth analysis and mining of a certain type of model. In addition, most studies overlook the importance of sub-model selection and ensemble mode in metal price forecasting, which can lead to poor forecasting results under some circumstances. To bridge these research gaps, a novel forecasting system including data pretreatment module, sub-model forecasting module, model selection module, and ensemble module, which successfully introduces a nonlinear ensemble mode and combines the optimal sub-model selection method, is developed for the non-ferrous metal prices futures market management. More specifically, data pretreatment is carried out to capture the main features of metal prices to effectively mitigate those challenges caused by noise. Then, the extreme learning machine series models are employed as the sub-model library and employed to predict the decomposed sub-sequences. Moreover, an optimal sub-model selection strategy is implemented according to the newly proposed comprehensive index to select the best model for each sub-sequence. Then, by proposing a nonlinear ensemble forecasting mode, the final point forecasting and uncertainty interval forecasting results are obtained based on the forecasting results of the optimal sub-model. Experimental simulations are carried out using the datasets copper and zinc, which show that the present system is superior to other benchmarks. Therefore, the system can be used not only as an effective technique for non-ferrous metal prices futures market management but also as an alternative for other forecasting applications.
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K P, Chandana. "Land Value Assessment and Blockchain Land Registry." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41215.
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The initiative focusing on land valuation and integrating blockchain technology into land registries aims to revolutionize land management practices by tackling inefficiencies, fraud, and a lack of transparency. The utilization of blockchain technology ensures the security and immutability of record-keeping. The project consists of four key modules: an administrative segment for registering landowners and generating QR codes, a buyer interface for accessing authenticated property details via QR codes, a landowner platform for verifying ownership using Aadhaar numbers, and a predictive module for estimating land prices employing the random forest algorithm to forecast prices based on user inputs. This integrated system streamlines land registration procedures, enhances transparency, reduces the risk of fraud, and fosters trust in transactions, offering benefits to administrators, buyers, and landowners. Keywords: Blockchain, Random Forest, Price Prediction
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Lee, In-Bae, Youngjin Kim, and Sojung Kim. "A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing." Machines 13, no. 4 (2025): 285. https://doi.org/10.3390/machines13040285.
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As renewable energy production grows, the photovoltaic (PV) module manufacturing process has received worldwide attention. In 2019, the total sales of PV modules were 1.7 billion U.S. dollars, and 78.7% of PV modules were made in South Korea. However, Korean manufacturers are facing high production costs due to high domestic labor costs and long-distance raw material procurement, making it difficult to produce price-competitive PV modules. In this situation, the best alternative for Korean manufacturers to gain a competitive edge is to produce high-quality PV modules. To this end, this study is going to propose a novel data-driven machine vision framework for the quality management of a PV manufacturing process consisting of seven stages, including tabbing, auto bussing, electro luminescence (EL), laminating, fame station, frame, and junction box. Particularly, the framework uses machine vision to analyze image data collected from an actual PV module manufacturing facility in South Korea. Autonomous decision-making algorithms are devised to recognize incorrect patterns of PV modules in terms of product quality. This experiment shows that the proposed framework enables the detection of PV module defects in electroluminescence (EL) and tabbing operations with a fault detection accuracy of over 95%. Therefore, the proposed framework enables a reduction in the number of defects, and this helps to improve quality loss during the PV module manufacturing process.
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Bormpotsis, Christos, Mohamed Sedky, and Asma Patel. "Predicting Forex Currency Fluctuations Using a Novel Bio-Inspired Modular Neural Network." Big Data and Cognitive Computing 7, no. 3 (2023): 152. http://dx.doi.org/10.3390/bdcc7030152.
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In the realm of foreign exchange (Forex) market predictions, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been commonly employed. However, these models often exhibit instability due to vulnerability to data perturbations attributed to their monolithic architecture. Hence, this study proposes a novel neuroscience-informed modular network that harnesses closing prices and sentiments from Yahoo Finance and Twitter APIs. Compared to monolithic methods, the objective is to advance the effectiveness of predicting price fluctuations in Euro to British Pound Sterling (EUR/GBP). The proposed model offers a unique methodology based on a reinvigorated modular CNN, replacing pooling layers with orthogonal kernel initialisation RNNs coupled with Monte Carlo Dropout (MCoRNNMCD). It integrates two pivotal modules: a convolutional simple RNN and a convolutional Gated Recurrent Unit (GRU). These modules incorporate orthogonal kernel initialisation and Monte Carlo Dropout techniques to mitigate overfitting, assessing each module’s uncertainty. The synthesis of these parallel feature extraction modules culminates in a three-layer Artificial Neural Network (ANN) decision-making module. Established on objective metrics like the Mean Square Error (MSE), rigorous evaluation underscores the proposed MCoRNNMCD–ANN’s exceptional performance. MCoRNNMCD–ANN surpasses single CNNs, LSTMs, GRUs, and the state-of-the-art hybrid BiCuDNNLSTM, CLSTM, CNN–LSTM, and LSTM–GRU in predicting hourly EUR/GBP closing price fluctuations.
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Park, Jeong Eun, Won Seok Choi, and Donggun Lim. "Cell/Module Integration Technology with Wire-Embedded EVA Sheet." Applied Sciences 11, no. 9 (2021): 4170. http://dx.doi.org/10.3390/app11094170.
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Silicon wafers are crucial for determining the price of solar cell modules. To reduce the manufacturing cost of photovoltaic devices, the thicknesses of wafers are reduced. However, the conventional module manufacturing method using the tabbing process has a disadvantage in that the cell is damaged because of the high temperature and pressure of the soldering process, which is complicated, thus increasing the process cost. Consequently, when the wafer is thinned, the breakage rate increases during the module process, resulting in a lower yield; further, the module performance decreases owing to cracks and thermal stress. To solve this problem, a module manufacturing method is proposed in which cells and wires are bonded through the lamination process. This method minimizes the thermal damage and mechanical stress applied to solar cells during the tabbing process, thereby manufacturing high-power modules. When adopting this method, the front electrode should be customized because it requires busbarless solar cells different from the existing busbar solar cells. Accordingly, the front electrode was designed using various simulation programs such as Griddler 2.5 and MathCAD, and the effect of the diameter and number of wires in contact with the front finger line of the solar cell on the module characteristics was analyzed. Consequently, the efficiency of the module manufactured with 12 wires and a wire diameter of 0.36 mm exhibited the highest efficiency at 20.28%. This is because even if the optical loss increases with the diameter of the wire, the series resistance considerably decreases rather than the loss of the short-circuit current, thereby improving the fill factor. The characteristics of the wire-embedded ethylene vinyl acetate (EVA) sheet module were confirmed to be better than those of the five busbar tabbing modules manufactured by the tabbing process; further, a high-power module that sufficiently compensated for the disadvantages of the tabbing module was manufactured.
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Qi, Minfeng, Ziyuan Wang, Shiping Chen, and Yang Xiang. "A Hybrid Incentive Mechanism for Decentralized Federated Learning." Distributed Ledger Technologies: Research and Practice 1, no. 1 (2022): 1–15. http://dx.doi.org/10.1145/3538226.
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Federated Learning (FL) presents a privacy-compliant approach by sharing model parameters instead of raw data. However, how to motivate data owners to participate in and stay within an FL ecosystem by continuously contributing their data to the FL model remains a challenge. In this article, we propose a hybrid incentive mechanism based on blockchain to address the above challenge. The proposed mechanism comprises two primary smart contract-based modules, namely the reputation module and the reverse auction module. The former is used to dynamically calculate the reputation score of each FL participant. It employs a trust-jointed reputation scheme to balance the weights between trust values of parameters and bid prices. The latter is responsible for initiating FL auction tasks, calculating price rankings, and assigning corresponding token rewards. Experiments are conducted to evaluate the feasibility and performance of the proposed mechanism against the three typical threats. Experimental results indicate that our mechanism can successfully reduce incentive costs while preventing participants from colluding and over-bidding in the data sharing auction.
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Wang, Huiwen, Ying Terk Lim, Shenming Xie, and Wen Yi. "Two-Stage Stochastic Programming for Precast Module Water Transportation: A Case Study in Hong Kong." Applied Sciences 14, no. 24 (2024): 11987. https://doi.org/10.3390/app142411987.
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Transporting precast modules via water is a vital component of multimodal transportation systems, increasingly utilized in large-scale Modular integrated Construction (MiC) projects where modules are prefabricated in remote factories. The effectiveness of module transportation planning significantly impacts the overall costs and productivity of MiC projects. However, existing studies on module transportation planning neglect the uncertainty inherent in MiC projects, thereby resulting in increased costs. This study proposes a two-stage stochastic programming model to optimize transportation planning through water, addressing this uncertainty. A real Hong Kong case study with 418 modules is employed to assess the effectiveness of the proposed model in comparison with three deterministic models. The optimal transportation plan of modules solved by the proposed model costs HKD 148,951, comprising 21% from temporary rentals and 79% from advance bookings. The results show that the three deterministic models, without considering the uncertainty in module demand, will incur additional transportation costs that are 25% higher on average than the results of the developed two-stage stochastic model. Additionally, this paper conducts a sensitivity analysis on the price ratio of pre-booked barges to on-demand barges to evaluate its impact on total transportation costs. The two-stage programming model developed in this paper can effectively help transport planners reduce the costs associated with module water transportation.
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He, Miao, Weiwei Jiang, and Weixi Gu. "TriChronoNet: Advancing electricity price prediction with Multi-module fusion." Applied Energy 371 (October 2024): 123626. http://dx.doi.org/10.1016/j.apenergy.2024.123626.
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Hermawan, Erwan, Raden Agung Wijono, Adiarso Adiarso, et al. "Solar Cell Manufacturing Cost Analysis and its Impact to Solar Power Electricity Price in Indonesia." International Journal of Energy Economics and Policy 13, no. 6 (2023): 244–58. http://dx.doi.org/10.32479/ijeep.14970.
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World energy consumption continues to increase, with a growth of 1,3% annually during 2011 - 2021. To deal with that situation, in 2021, Indonesia Electrity Stated-Own Company (PLN) issued a report about the electricity supply business plan (RUPTL) 2021-2030. The power plant projected to grow is solar photovoltaic (PV), reaching 4,6 GW within ten years. However, in reality, the achievement of developing solar power plants is still 0,02 GW. Several factors that caused low realization are the solar power plant component industry needs to be better developed, and the regulated electricity price based on Presidential Decree 112/2022 needs to be increased. This study aims to determine the cost structure of solar module manufacturing and the impact on electricity prices. The calculation method used is financial modeling with economic parameters such as Internal Rate of Return (IRR), payback period, and Net Present Value (NPV) as parameters for project feasibility. The results of this study show that the economic price of solar power plants in Indonesia is USD 0,149/kWh. Meanwhile, based on a sensitivity analysis using electricity prices based on Presidential Decree, reducing solar module costs up to 50% still does not make the project feasible.
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Zhu, Chun Dong, Wei Tian, Xuan Zhang, and Wei Fan Yin. "Parametric Design of Friction Plate Based on VB and Pro/E." Advanced Materials Research 655-657 (January 2013): 277–80. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.277.
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This paper introduces the secondary development of Pro/E with VB , established the parametric design software of friction plate which including part design module and quotation module. In parts design module user can input parts parameters for automatically model regeneration in Pro/E. Quotation module solved the common problems such as long time-consuming and inaccurate calculation, provided a more convenient, fast and practical tool for quoted price.
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Li, Yuhong, Nan Yang, Guihong Bi, Shiyu Chen, Zhao Luo, and Xin Shen. "Carbon Price Forecasting Using a Hybrid Deep Learning Model: TKMixer-BiGRU-SA." Symmetry 17, no. 6 (2025): 962. https://doi.org/10.3390/sym17060962.
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As a core strategy for carbon emission reduction, carbon trading plays a critical role in policy guidance and market stability. Accurate forecasting of carbon prices is essential, yet remains challenging due to the nonlinear, non-stationary, noisy, and uncertain nature of carbon price time series. To address this, this paper proposes a novel hybrid deep learning framework that integrates dual-mode decomposition and a TKMixer-BiGRU-SA model for carbon price prediction. First, external variables with high correlation to carbon prices are identified through correlation analysis and incorporated as inputs. Then, the carbon price series is decomposed using Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) to extract multi-scale features embedded in the original data. The core prediction model, TKMixer-BiGRU-SA Net, comprises three integrated branches: the first processes the raw carbon price and highly relevant external time series, and the second and third process multi-scale components obtained from VMD and EWT, respectively. The proposed model embeds Kolmogorov–Arnold Networks (KANs) into the Time-Series Mixer (TSMixer) module, replacing the conventional time-mapping layer to form the TKMixer module. Each branch alternately applies the TKMixer along the temporal and feature-channel dimensions to capture dependencies across time steps and variables. Hierarchical nonlinear transformations enhance higher-order feature interactions and improve nonlinear modeling capability. Additionally, the BiGRU component captures bidirectional long-term dependencies, while the Self-Attention (SA) mechanism adaptively weights critical features for integrated prediction. This architecture is designed to uncover global fluctuation patterns in carbon prices, multi-scale component behaviors, and external factor correlations, thereby enabling autonomous learning and the prediction of complex non-stationary and nonlinear price dynamics. Empirical evaluations using data from the EU Emission Allowance (EUA) and Hubei Emission Allowance (HBEA) demonstrate the model’s high accuracy in both single-step and multi-step forecasting tasks. For example, the eMAPE of EUA predictions for 1–4 step forecasts are 0.2081%, 0.5660%, 0.8293%, and 1.1063%, respectively—outperforming benchmark models and confirming the proposed method’s effectiveness and robustness. This study provides a novel approach to carbon price forecasting with practical implications for market regulation and decision-making.
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Zhang, Zuo, Xinhai Lu, Min Zhou, Yan Song, Xiang Luo, and Bing Kuang. "Complex Spatial Morphology of Urban Housing Price Based on Digital Elevation Model: A Case Study of Wuhan City, China." Sustainability 11, no. 2 (2019): 348. http://dx.doi.org/10.3390/su11020348.
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In a city, housing price varies with location. Thus, housing price plays an important role in detecting the spatial pattern of the city. Spatial interpolation methods have been widely used for simulating and predicting urban housing prices. In this paper, the Ordinary Kriging interpolation method is used for producing the digital elevation model (DEM) of urban housing prices. Based on the three-dimensional DEM of urban housing price, this paper develops a novel approach for geo–visual analytics of urban housing prices. To investigate and visualize the spatial morphology of housing price, we design the Water-flooding, Section-cutting and Belt-floating methods, and implement these methods with the 3D-analyst module in GIS environment. Then, we take Wuhan City as a case, apply this approach to analyze the complex spatial morphologic characteristics of the DEM for housing price and visualize the results from the multidimensional perspectives. The results show that the Water-flooding method effectively supports the investigation of the top areas of surface changes; Section-cutting method performs well in examining the profile or cross-section of the urban housing surface; and Belt-floating method is helpful for detecting the spatial variance of the urban housing surface through the routes of specific lines. The results demonstrate that the proposed approach works better than traditional methods in describing the complex spatial morphology of urban housing prices, and has an advantage in visualizing the analysis results.
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Rahman, Hanung Pangestu, Jamaludin Indra, and Rahmat Rahmat. "Penerapan Convolutional Neural Network pada Timbangan Pintar Menggunakan ESP32-CAM." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 283. http://dx.doi.org/10.30865/mib.v7i1.5469.
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Scales are needed by traders, including vegetable traders, but the scales created in the market can only determine the weight. That way traders need time to calculate the price based on the weight and type of vegetables. In previous research there has been research on smart scales that can calculate the total price based on the weight and type of vegetables being weighed, this study used the Raspberry Pi 3 Model B and the Convolutional Neural Network (CNN) as a method for the scales to be able to identify the types of vegetables that are on it. Along with the rapid development of technology, the price of the Raspberry Pi for all variants has increased in price. Therefore the need for research on smart scales with components that have relatively cheaper prices. In this study, researchers used the ESP32-CAM microcontroller, which is priced relatively cheaper than the Raspberry Pi 3 Model B. This research still uses the Convolutional Neural Network (CNN) method and a load cell equipped with the HX711 module as a sensor to obtain the weight value of an object. The dataset collected totaled 600 image data with 150 image data for each type of vegetable, classes in the training data consisted of tomatoes, cabbage, carrots, and potatoes. Smart scales using the ESP32-CAM get results of a classification accuracy of 90% and the average difference of the tools built is 0.8 grams compared to the SF-400 brand digital scales.
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Kimsong, Suy, Horchhong Cheng, Chivon Choeung, Sophea Nam, and Darith Leng. "Improvement of Solar Farm Performance based on Photovoltaic Modules Selection." International Journal of Electrical and Electronics Research 12, no. 3 (2024): 951–57. http://dx.doi.org/10.37391/ijeer.120328.
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The emissions of greenhouse gases from conventional power plants are currently a significant cause for worry. In China, about 75% of total domestic energy is dependent on coal-fire power, which emits 50% of total SO2 and has a significant impact on the human respiratory system. Therefore, solar power plants are a viable option that can mitigate this problem. Furthermore, the efficiency of solar modules exhibits a progressive upward trend, while their price per watt experiences a corresponding decline, making it a promising source for future energy. This article examines the performance and effectiveness of several photovoltaic (PV) modules in designing solar plants on a certain land area measuring 10000 m2 (100 m * 100 m). The PV plant performance was evaluated by comparing occupation ratio (OR), PV power capacity, net energy production, performance ratio (PR) via PVsyst software, and lastly financial analysis. Consequently, the PV module (PV7), characterized by its high efficiency, low temperature coefficients, and affordable price, result in a significant OR (73.81%), increased installed PV power capacity (1568kW), enhanced net energy output (2269029 kWh/year), improved yearly PR (83.4%), and lastly, the shortest payback period (around two years). Instead of optimizing shadow length in existing research, this paper aims to improve the performance of large-scale solar farm based on PV module selection which results in less computation and structure installation efforts.
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Kopecek, Radovan, and Joris Libal. "Bifacial Photovoltaics 2021: Status, Opportunities and Challenges." Energies 14, no. 8 (2021): 2076. http://dx.doi.org/10.3390/en14082076.
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In this paper we summarize the status of bifacial photovoltaics (PV) and explain why the move to bifaciality is unavoidable when it comes to e.g., lowest electricity generation costs or agricultural PV (AgriPV). Bifacial modules—those that are sensitive to light incident from both sides—are finally available at the same price per watt peak as their standard monofacial equivalents. The reason for this is that bifacial solar cells are the result of an evolution of crystalline Si PV cell technology and, at the same time, module producers are increasingly switching to double glass modules anyway due to the improved module lifetimes, which allows them to offer longer product warrantees. We describe the general properties of the state-of-the-art bifacial module, review the different bifacial solar cells and module technologies available on the market, and summarize their average costs. Adding complexity to a module comes with the increase of possible degradation mechanisms, requiring more thorough testing, e.g., for rear side PID (Potential Induced Degradation). We show that with the use of bifacial modules in fixed tilt systems, gains in annual energy yield of up to 30% can be expected compared to the monofacial equivalent. With the combination of bifacial modules in simple single axis tracking systems, energy yield increases of more than 40% can be expected compared to fixed tilt monofacial installations. Rudimentary simulations of bifacial systems can be performed with commercially available programs. However, when more detailed and precise simulations are required, it is necessary to use more advanced programs such as those developed at several institutes. All in all, as bifacial PV—being the most cost-effective PV solution—is now becoming also bankable, it is becoming the overall best technology for electricity generation.
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Priyadi, Irnanda, Faisal Hadi, Doni Tamara, and Makmun Reza Razali. "PERANCANGAN ELEKTRO EJAKULATOR SEBAGAI ALAT BANTU INSEMINASI BUATAN PADA KAMBING TERNAK DI KOTA BENGKULU." Teknosia 16, no. 1 (2022): 1–13. http://dx.doi.org/10.33369/teknosia.v16i1.21086.
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Semen collection is one of the activities contained in the artificial insemination program in animals. Semen collection can be done in several ways, namely using an artificial vagina, massage and using an electro ejaculator module. The use of the electro ejaculator module is a safe and appropriate method for male livestock. Electro ejaculator module sold in the market has a relatively expensive price and is still difficult to find, especially in Indonesia. Therefore, an electro ejaculator module or device for goats is needed that has an affordable price so that it can be used by small to medium-sized farms. This electro ejaculator device consists of 3 main parts, namely Switch Mode Power Supply, function generator and an electro ejaculatory probe. At the testing stage of the switch mode power supply (SMPS) section, the output voltage variation is obtained from 0 - 15 volts. While in the function generator section, the output signal is in the form of a sinusoidal which can be adjusted for the amplitude and the output frequency from 0 - 1 Mhz. Based on these results, the designed Electro Ejaculator module can be applied to assist the process of artificial insemination in animals.
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Velte, Andreas, Lukas Joos, and Gerrit Füldner. "Experimental Performance Analysis of Adsorption Modules with Sintered Aluminium Fiber Heat Exchangers and SAPO-34-Water Working Pair for Gas-Driven Heat Pumps: Influence of Evaporator Size, Temperatures, and Half Cycle Times." Energies 15, no. 8 (2022): 2823. http://dx.doi.org/10.3390/en15082823.
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A major challenge for gas-driven adsorption heat pumps is the production of compact, efficient, and cost-effective adsorption modules. We present the experimental data of a design based on sintered aluminum fiber heat exchangers, a technology currently under development. The adsorption module presented here is the result of the downsizing of a larger module. The downsized module has an adsorption heat exchanger that is 60% of the size of the larger-scale component, and an evaporator-condenser that is only 30% of the size of the larger-scale component. It is designed to fit the heating requirements of a wall-hung heat pump for a single-family home. For the first time, a comprehensive experimental study of the influence of half-cycle time, evaporator and adsorption temperature, and driving temperature on the efficiency and power of the module is presented. At temperature conditions relevant for the application of a gas-driven adsorption heat pump, i.e., evaporator temperature < 10 °C and adsorption temperature > 30 °C, we found that the downsizing has its price in terms of a higher thermal capacity of the components in relation to the adsorbent mass (9.6 kJ/(kg∙K) for ‘Size S’) vs. 5.6 kJ/(kg∙K) for ‘Size L’). We carried out an evaluation of heat and mass transfer resistances to compare the ‘Size L’ module directly with the ‘Size S’ module. Both modules have nearly the same volume-scaled heat and mass transfer resistances of 0.012 dm3 K/W (adsorption heat exchanger during adsorption) and 0.005 dm3 K/W (evaporator–condenser during evaporation), and consequently a very similar volumetric power density. This evaluation proves the applicability and the consistency of the concept of heat and mass transfer resistances, and the scalability of this adsorption module technology.
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Niu, Dongyin, Tiantian Zhang, Yufei Tan, and Wensheng Zhang. "Numerical Research on the Performance of a Phase Change Heat Storage Electric Heating Module." E3S Web of Conferences 356 (2022): 01045. http://dx.doi.org/10.1051/e3sconf/202235601045.
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A kind of phase change heat storage electric heating modules filled with the composite PCM was designed and fabricated in this paper. The thermal performance of the module was studied through the experiments, and the thermal performance influencing factors were studied using the FLUENT software. Simulation results indicated that the optimal thermal conductivity of the PCM should be between 3 W/(m·K) and 5W/(m·K). With the designed arrangement of the heating wire, the optimal electric heating power should be between 9.00W/m and 9.85W/m, and the corresponding adjustments should be made according to the local electricity price policy. When the electric heating power is 9.85W/m and the thermal conductivity is 4W/(m·K), the distance between the heating wire should be between 40mm and 45mm, which can ensure the high heat-storage efficiency and heating continuity. In general, the internal temperature of the module is relatively uniform and can maintain stable heat dissipation within the effective heating range for a long time. The temperature fluctuation of the module heat dissipating surface is small. The module has good thermal performance and can meet the heating requirements when being applied to electric radiant heating in the building.
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Jagtap, Aviraj. "Slickdeals Aggregator Web-Application Using Web Scraping." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1756–60. http://dx.doi.org/10.22214/ijraset.2023.56913.
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Abstract: Due to the e-commerce industry's explosive growth, there are a large number of online retail platforms that sell a wide variety of goods. Because there are more products and shops available, it can be difficult for customers to compare prices and discounts among different online retailers. It describes how to create a price comparison website that gathers and compares product prices from various e-commerce websites using web scraping techniques. The system uses web scraping to collect product information such as price, reviews, offers, descriptions and availability from certain e- commerce websites. For this purpose, the website is designed with a web scraping module that automates data extraction using Python modules such as Beautiful Soup and Selenium. The web interface primarily helps users make informed purchasing decisions by allowing them to search for products and compare prices. The first issue this article addresses is the content on the website, followed by the oversight of accurate information and the provision of content related to the website's access process. Web scraping's ethical concerns are also covered, with an emphasis on responsible data use and adherence to e-commerce platforms' intellectual property and privacy rights. It demonstrates the value of a pricecomparison website in giving users access to current pricing data from several online retailers. The service makes use of web scraping technologies to make online buying easier and give users the knowledge they need to make wise financial decisions
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Sun, Qing Juan, and Jian Zheng Zhou. "Research on the Application of VR in the Model of Grading and Evaluating of Farmland Based on GIS." Applied Mechanics and Materials 312 (February 2013): 849–52. http://dx.doi.org/10.4028/www.scientific.net/amm.312.849.
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Virtual Reality (VR) is to immerse interactivity and imagination as the essential features of the advanced computer interface. The grading and evaluation work of agricultural land involves a large number of processing, statistics, analysis and calculation of spatial and attribute data, and it is a good idea to adopt the most advanced computer technologyGIS technology and database technology to eatablish the model of the farmland scientific and reasonable grading and evaluating . The model includes the module for land requisitionthe module for land requisitionthe module for land classification and modulefor the benchmark land price evaluation module.
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Guan, Renchu, Aoqing Wang, Yanchun Liang, Jiasheng Fu, and Xiaosong Han. "International Natural Gas Price Trends Prediction with Historical Prices and Related News." Energies 15, no. 10 (2022): 3573. http://dx.doi.org/10.3390/en15103573.
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Under the idea of low carbon economy, natural gas has drawn widely attention all over the world and becomes one of the fastest growing energies because of its clean, high calorific value, and environmental protection properties. However, policy and political factors, supply-demand relationship and hurricanes can cause the jump in natural gas prices volatility. To address this issue, a deep learning model based on oil and gas news is proposed to predict natural gas price trends in this paper. In this model, news text embedding is conducted by BERT-Base, Uncased on natural gas-related news. Attention model is adopted to balance the weight of the news vector. Meanwhile, corresponding natural gas price embedding is conducted by a BiLSTM module. The Attention-weighted news vectors and price embedding are the inputs of the fused network with transformer is built. BiLSTM is used to extract used price information related with news features. Transformer is employed to capture time series trend of mixed features. Finally, the network achieves an accuracy as 79%, and the performance is better than most traditional machine learning algorithms.
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Huang, Anzhong, Hong Chen, Xuan Hu, and Luote Dai. "The Analysis of Enterprise Improvement in Global Commodity Price Prediction Based on Deep Learning." Journal of Global Information Management 31, no. 3 (2023): 1–20. http://dx.doi.org/10.4018/jgim.321115.
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The article expects to solve the traditional econometric statistical model, shallow machine learning algorithm, and many limitations in learning the nonlinear relationship of related indicators affecting commodity futures price trend. This article proposes a neural network commodity futures price prediction model by the mixture of convolutional neural networks (CNN) and gated recurrent unit (GRU). Firstly, the dimension reduction algorithm of multidimensional data by principal component analysis (PCA) is used. Through linear transformation, the original variables with correlation are transformed into a set of new linear irrelevant variables, and the high-dimensional time series data of commodity futures are reduced. Secondly, the variable features are extracted from the CNN network module in the CNN-GRU model, and the GRU network module learns the periodicity and trend of the original data. Finally, the full connection layer outputs the forecast results of commodity futures price.
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Chen, Bo, Ze Hu, Liang Ge, and Jun Lan Li. "Design of Wireless Fire Detection and Alarm System Based on ZigBee Technology." Applied Mechanics and Materials 448-453 (October 2013): 3662–65. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3662.
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In order to transmit temperature and smoke signals timely and accurately in the fire alarm system of community, design one kind of wireless fire detection and alarm system, which is based on the wireless communication technology, ZigBee. When acquisition module receive the signals which come from the wireless data acquisition module which connected the room with temperature detection module, and then, send to the nearest backbone transceiver module, the defined center module receive the signals, at last, the signals are local-stored or transferred to fire center by GPRS. This design with the characters of low price, high efficiency and reliable, has a certain market.
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Wang, Shuai Feng, Liang Hou, Hao Lun Wang, and Wen Guang Lin. "Analysis Method for Commonality of Module and Part in Modular Product Family." Advanced Materials Research 201-203 (February 2011): 1425–28. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.1425.
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An analysis method for commonality of module & part in modular product family was put forward. The part and module commonality in module layer and component part layer of product family were identified, respectively. The formulations of the two commonalities take into account amount of component part or module, variety, volume, price/cost of the part or module, size, geometry, material, manufacturing process, assembly. According to the source of parts, the mathematical formulas of self-made parts and purchased parts were set up respectively in the component part layer. Finally, an example of drive axle of wheel loader was given to indicate the effectiveness of the proposed method.
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Xu, Hang. "Using Kernel Method to Include Firm Correlation for Stock Price Prediction." Computational Intelligence and Neuroscience 2022 (April 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/4964394.
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In this work, we propose AGKN (attention-based graph learning kernel network), a novel framework to incorporate information of correlated firms of a target stock for its price prediction in an end-to-end way. We first construct a stock-axis attention module to extract dynamic and asymmetric spatial correlations through the kernel method and a graph learning module into which more accurate information can be integrated. An ensemble time-axis attention module is then applied to learn temporal correlations within each stock and market index. Finally, we utilize a transformer encoder to jointly attend to obtain information from different levels for correlations’ aggregation and prediction. Experiments with data collected from the Chinese stock market show that AGKN outperforms state-of-the-art baseline methods, making up to 4.3% lower error than the best competitors. The ablation study shows that AGKN pays more attention to hidden correlation between stocks, which improves model’s performance greatly.
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Kozinsky, Inna, Brion Bob, and Rebecca Jones-Albertus. "Challenges and Opportunities for Improving Thin-Film Photovoltaics." MRS Advances 1, no. 41 (2016): 2827–32. http://dx.doi.org/10.1557/adv.2016.436.
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ABSTRACTThe U.S. Department of Energy (DOE)’s SunShot Initiative is a collaborative national effort to reduce the price of solar energy to 6¢/kWh without subsidy for utility scale installations by 2020. Thin-film photovoltaics offer a promising path to reach this goal. Analysis of the levelized cost of energy (LCOE) from photovoltaics (PV) highlights the dependence on the module and system efficiency and lifetime in addition to module price. Here we summarize challenges and opportunities for CdTe and CIGS PV research and show that a substantial effort is still needed in areas such as device design and material improvement to reach higher efficiency and reliability connected with low-cost and robust module-scale implementation. We also discuss how SunShot Initiative funding is addressing key research areas in CdTe and CIGS PV and show how recent progress in SunShot projects is guiding funding priorities in thin-film PV research.
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Posadas-Yagüe, Juan-Luis. "Low-cost modular devices for on-road vehicle detection and characterisation." Design Automation for Embedded Systems 27 (June 5, 2023): 85–102. https://doi.org/10.1007/s10617-023-09270-y.
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Detecting and characterising vehicles is one of the purposes of embedded systems used in intelligent environments. An analysis of a vehicle’s characteristics can reveal inappropriate or dangerous behaviour. This detection makes it possible to sanction or notify emergency services to take early and practical actions. Vehicle detection and characterisation systems employ complex sensors such as video cameras, especially in urban environments. These sensors provide high precision and performance, although the price and computational requirements are proportional to their accuracy. These sensors offer high accuracy, but the price and computational requirements are directly proportional to their performance. This article introduces a system based on modular devices that is economical and has a low compu- tational cost. These devices use ultrasonic sensors to detect the speed and length of vehicles. The measurement accuracy is improved through the collaboration of the device modules. The experiments were performed using multiple modules oriented to different angles. This module is coupled with another specifically designed to detect distance using previous mod- ules’ speed and length data. The collaboration between different modules reduces the speed relative error ranges from 1 to 5%, depending on the angle configuration used in the modules.
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Cui, Jian Tao. "Design and Implementation of Computer Network Monitoring Software." Applied Mechanics and Materials 686 (October 2014): 201–4. http://dx.doi.org/10.4028/www.scientific.net/amm.686.201.
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This paper mainly studies the key technologies of network real-time monitoring system based on Client/Server, and implementation of a real-time monitoring system based on CS/ mode. Using the network communication technology, Winsock technology, TCP/IP protocol, image compression and transmission technology, the process of communication technology and object oriented software technology to realize the main frame module, the system include network monitoring data initialization module, data transmission module, image coding and decoding module, its advantage is to make full use of the existing network resources, the highest price, with real-time information control and real-time control as the center, timely delivery and management of information.
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Guo, Miao Yun, Tian Ding Chen, and Chang Hong Yu. "A Novel Embedded System for Wireless Ordering Based on RFID." Key Engineering Materials 439-440 (June 2010): 251–56. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.251.
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As the development of the electric technology, more and more embedded system are applied in all walks of life. This paper describes an embedded system for wireless ordering, which can be used in many fields, besides restaurants. The system is consisted of LCD display module, wireless communication net module which includes sending and decoding. This design has advantages of both low price and high performance.
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Yang, Shu, Jinglin Li, Quan Yuan, Zhihan Liu, and Fangchun Yang. "Message Relaying and Collaboration Motivating for Mobile Crowdsensing Service: An Edge-Assisted Approach." Wireless Communications and Mobile Computing 2018 (July 29, 2018): 1–13. http://dx.doi.org/10.1155/2018/1287969.
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Group sensing is a kind of crowdsensing service where HD map producers motivate private cars in a local region to collect data from real world. Group sensing needs vehicles to communicate physically and drivers to collaborate strategically in a mobile or edge-assisted environment. First, we consider collaboration module that motivates drivers to be participants; centralized and distributed motivating methods are discussed. Secondly, we consider communication module; two VANET-based methods are proposed to achieve message relaying in edge infrastructure. To accomplish participants’ selection, three combinations of two modules are proposed and simulated based on a flexible framework. The results show that centralized selection could motivate collaboration at a low price but brings heavy communication overhead. Clustered selection requires more incentives and less communication overhead than centralized selection. Distributed selection is usually the first class choice because of its fine performances on both communicating and motivating.
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Pan, Qingyi, Suyu Sun, Pei Yang, and Jingyi Zhang. "FuturesNet: Capturing Patterns of Price Fluctuations in Domestic Futures Trading." Electronics 13, no. 22 (2024): 4482. http://dx.doi.org/10.3390/electronics13224482.
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Futures trading analysis plays a pivotal role in the development of macroeconomic policies and corporate strategy planning. High-frequency futures data, typically presented as time series, contain valuable historical patterns. To address challenges such as non-stationary in modeling futures prices, we propose a novel architecture called FuturesNet, which uses an InceptionTime module to capture the short-term fluctuations between ask and bid orders, as well as a long-short-term-memory (LSTM) module with skip connections to capture long-term temporal dependencies. We evaluated the performance of FuturesNet using datasets numbered 50, 300, and 500 from the domestic financial market. The comprehensive experimental results show that FuturesNet outperforms other competitive baselines in most settings. Additionally, we conducted ablation studies to interpret the behaviors of FuturesNet. Our code and collected futures datasets are released.
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Nurmaida, Firnanda Pristiana, Agus Indra Gunawan, Teguh Hady Ariwibowo, et al. "Implementasi Modul Water Quality Meter pada Komunitas Petani Udang Vaname Jawa Timur." GUYUB: Journal of Community Engagement 5, no. 1 (2024): 136–52. http://dx.doi.org/10.33650/guyub.v5i1.7942.
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Vannamei shrimp is one of the leading commodities in fisheries aquaculture, due to its competitive price and the ability to be mass-produced with high stocking densities. Many coastal communities in East Java capitalize on this opportunity by engaging in vannamei shrimp cultivation. However, most shrimp farmers still measure pond water quality using conventional methods and record water quality parameters on paper, which is highly inefficient. With this issue in mind, the author sought to engage in community service by inviting representatives from the East Java vannamei shrimp farming community. The method involved delivering lectures and interactive discussions with shrimp farmers to understand their perceptions and insights regarding pond water quality, followed by the handover of modules, and subsequently evaluating the modules' usage by the shrimp farmers. As a result of this community service, we introduced a tool to assist traditional shrimp pond farmers in monitoring water quality, in the form of a "Water Quality Meter" module integrated with a website accessible via smartphones and laptops. The "Water Quality Meter" module was designed with a system to portable monitor pond water quality using Internet of Things (IoT) technology, where data obtained by microcontrollers is transmitted to a database to determine the quality value of pond water. Evaluation results indicate that farmers can use the module effectively, and data collected on the website shows that pond water quality for the farmers remains within normal ranges. Shrimp farmers directly benefit from using the module, as the shrimp pond monitoring process becomes more practical and accurate.
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Chen, Li Gang, Xue Feng Yang, and Bo Qu. "Design of Intelligent Locomotive Based on STC12C5A60S2." Advanced Materials Research 912-914 (April 2014): 617–20. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.617.
Full textAbstract:
To solve the intelligent locomotive can be turn around precisely on the preset orbit by others, It has the ability to identify the environment accurately, such as surrounding temperature, solid distance and color, the humidity of the environment and so on.Combined with the price, A method is proposed by using STC12C5A60S2 microcontroller as the core control of the intelligent vehicle.We use the infrared tube module tracking black track, it can turn 90 degrees or 360 degrees precisely when it track to a positioning point. To accurately detect point target by adopting the temperature detection module, ultrasonic distance measuring module, a color recognition module, humidity detection module, it can be real time display data by LCD module 12864,it can be reach the end of accurate parking .The experimental results show that it completely meets the requirements, This method is simple, accurate and efficient.