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Scientific and technological forecasting for a long run is essential when determining priorities in science, engineering, and technology development. The forecasting methods used in its development and based on the Delphi approach cannot provide a complete analysis of development trends, depending on the experts’ professionalism, and allow facts of lobbying interests of various groups. In this paper, the mechanisms for improving the quality of scientific and technological forecasts by using the theory of inventive problem solving (TIPS) when forming and verifying forecasts have been offered. In this case, the forecast results are based not only on the expert’s subjective opinion but also on the objective laws of the development of the technical systems. The material has come with detailed practical examples. The integration of the TRIZ instruments in the state system for determining top priority technological areas will reduce errors in choosing the number of promising technological and technical areas that will make up the basis for the formation of new models and technologies.

The deformation of a part occurring in the process of grinding directly influences its exploitation and quality parameters. The instability of shape and size, which occurs due to an imbalance of residual stress, can be the one of the major causes of deformation of a part. The decrease in stress slows down the deformation process. Considering the regularities of heat source intensity dependence on the grinding modes, it can be asserted that with increasing grinding depth and grinding wheel hardness, the value increases and it decreases with a growth in a speed of the part and the use of cooling. The higher the heat removal is and the better lubricant properties of the liquid are, the more significant the decrease in is. Changing these values allows regulation of the residual stresses. As a result of the research on determination of deformations, it is recommended to reduce thermal deformations by considering the geometric size of a plate to be machined, linear expansion coefficient of plate material and an allowance for nonflatness from thermal deformations. The value of nonflatness from thermal deformations is directly proportional to linear expansion coefficient of plate material and its square overall dimensions. At the same time, the value of nonflatness is inversely proportional to the plate thickness.

En el presente trabajo se realiza un análisis utilizando la base de datos de la Oficina de Patentes y Marcas de Estados Unidos, considerada la más extensa e importante a nivel mundial, revisando las patentes registradas por los principales fabricantes y desarrolladores de tecnología de fabricación aditiva a nivel industrial y doméstico, para realizar un pronóstico tecnológico que ayude a identificar la tendencia que tendrá el desarrollo de la fabricación aditiva, visualizando cual será el principio de fabricación por esta herramienta tecnológica, que se convertirá en el estándar de la industria y que predomine en el mercado. En los resultados obtenidos en este análisis, todo parece indicar que la fabricación por deposición de plástico fundido, se convertirá en la tecnología dominante en este segmento de la industria.

The PUREX process has been one of the most successful methods to reprocess spent nuclear fuel for several decades, and further enhancements have being carried out in the worldwide. The published global patents about the PUREX technology are investigated in this work. The technology forecasting and the development strategy of PUREX process was provided from the view of patents, based on the nations, the technology field, the trend in the number of patent applications. The purpose of this work is to help the technical personnel in this field to understand the advanced development of PUREX technology from a new perspective.

The history of aluminum is rather short since it was discovered only in the nineteenth century, yet it has become an important part of everyday life. This article reviews the history of aluminum through technological breakthroughs as well as from cultural and social perspectives, beginning with its discovery, through the nineteenth and twentieth centuries until today; and aims to suggest possible future trends and applications for aluminum alloys. Aluminum has a high strength-to-weight ratio combined with excellent thermal conductivity and good corrosion resistance. Therefore, aluminum is an attractive material for many applications, including transportation, electrical and packaging industries, architecture, and food industries. It is also a recyclable metal, which provides both environmental and economic advantages. The commercial use of aluminum started at the end of the nineteenth century and continues to grow today with the development of new advanced aluminum alloys. Consequently, from a cultural perspective, aluminum is considered a symbol of modernity. Technological breakthroughs generate economic growth and social benefits. Present applications of aluminum include new choices, such as 3D printing, composite materials, nano-rods, biomedicine devices and aerospace uses. Based on the excellent properties of aluminum, its low price, combined with its significant scrap value and a growing recycling market, as well as its accelerating global production, it is expected that the aluminum industries will considerably grow through the twenty-first century and aluminum will continue to be a major part of our everyday culture. Therefore, based on the increasing growth of aluminum production and consumption, additional research and development effort is needed in the following years to minimize the negative environmental side effects associated with the technological developments related to aluminum production and at the same time creating further technological innovation.

The case study examines a number of tools that are used within the knowledge management and technology forecasting realm. Also, it gives an overview of how Daimler Trucks North America currently practices knowledge management and technology forecasting. Special attention is given to sharing knowledge inside the company and across company borders for different purposes. It is shown how effective knowledge management and technology forecasting is designed and implemented.

The future of power electronics in 2025 and beyond was the focus of session five of the Future of Electronic Power Processing and Conversion (FEPPCON) IX, which was held in June in Kruger Park, South Africa. This session was organized into five presentations, the first of which was given by Fred Lee, a U.S. National Academy of Engineering member and a distinguished professor at Virginia Tech. He described how to gain a better understanding of the modular multilevel converter (MMC) control to minimize the circulating current. This method has the potential to unleash the full power of MMCs. Prof. Zhengming Zhao of Tsinghua University, and the principal investigator of the Vitally Important Technology Development Program, sponsored by the National Natural Science Foundation of China, gave the second demonstration. He discussed the challenges facing megawatt power electronic circuit design and hardware development, especially in transient conditions. Afterward, the third lecture was given by Don Tan, distinguished engineer of Northrop Grumman Aerospace Systems. His talk presented two challenges that are facing power electronics communities for megawatt simulation and design. Dianguo Xu, vice president and professor of Harbin Institute of Technology's presentation was next and highlighted the potential challenges in connected motor drives and their impacts on the society at large. Finally, Ron Hui and C.K. Lee, both from Hong Kong University, gave the last presentation. Prof. Hui is a member of British Royal Society. They examined energy harvesting and wireless power transfer.

Multistage expert surveys like the Delphi method are proven concepts for technology forecasting that enable the prediction of content-related and temporal development in fields of innovation (e.g., [1, 2]). Advantages of these qualitative multistage methods are a simple and easy to understand concept while still delivering valid results [3]. Nevertheless, the literature also points out certain disadvantages especially in large-scale technology forecasts in particularly abstract fields of innovation [4]. The proposed approach highlights the usefulness of the repertory grid method as an alternative for technology forecasting and as a first step for preference measurement. The basic approach from Baier and Kohler [5] is modified in-so-far that an online survey reduces the cognitive burden for the experts and simplifies the data collection process. Advantages over alternative approaches through its simple structure and through combining qualitative and quantitative methods are shown and an adaption on an actual field of innovation – civil drones in Germany – is done. The measurement of a common terminology for all experts minimizes misunderstandings during the interview and the achievement of an inter-individual comparable level of abstraction is forced by the laddering technique [6] during the interview.

There are a large number of accounts about rapidly declining costs of batteries with potentially transformative effects, but these accounts often are not based on detailed design and technical information. Using a method ideally suited for that purpose, we find that when experts are free to assume any battery pack design, a majority of the cost estimates are consistent with the ranges reported in the literature, although the range is notably large. However, we also find that 55% of relevant experts’ component-level cost projections are inconsistent with their total pack-level projections, and 55% of relevant experts’ elicited cost projections are inconsistent with the cost projections generated by putting their design- and process-level assumptions into our process-based cost model (PBCM). These results suggest a need for better understanding of the technical assumptions driving popular consensus regarding future costs. Approaches focusing on technological details first, followed by non-aggregated and systemic cost estimates while keeping the experts aware of any discrepancies, should they arise, may result in more accurate forecasts.

Thomas Friedman's recent book, Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations, traces how the technology revolution, fueled by the acceleration predicted in Moore's law, has transformed our lives [1]. Friedman celebrates the remarkable advances that have occurred over the past several decades and describes, in layman's terms, the ways in which the solidstate circuits community is transforming diverse fields ranging from computing to transportation, communications to robotics, and entertainment to energy.

The problem of product service forecasting in hierarchical service structure is common in many contexts. It is common that the results of the higher level forecasting are in conflict with the sum of the lower level forecasts. The phenomenon has confused the managers in decision making. This aim of this research is to forecast the product services demands in a hierarchical service structure context. By integrating the methods combination forecasting approach and the information combination forecasting approach, the paper provide a multi-level combined forecasting model with six steps. A case of air compressor services forecasting is employed to verify the developed approach. The in-sample and out-of-sample test indicates that the proposed model outperforms the individual models and the direct combination of individual methods. The proposed method could be easily customized for solving other product service forecasting problems, especially when the hierarchical time series data are involved.

Utilization of renewable energy technologies is one of the strate-gies of the governments in order to increase the level of energy security. One of the main renewable energy resources with fast utilization growth is wind power. However, development of wind power is highly de-pended on its related technologºies. Therefore, wind power technology forecasting is an indispensable requirement to understand how each of the wind energy domains will improve in the future. This research is to focus on the analysis of wind power technologies based on the technol-ogy life cycle approach. The contributions can help researchers, technol-ogy futurists, and policy makers to focus better on the related R&D and utilization policies.

To help celebrate software engineering’s 50th anniversary, department editor Mik Kersten considers how software engineering will evolve over the coming 50 years. His five predictions aren’t intended to be precise; they aim to provide discussion topics for the shape of software engineering trends to come. This article is part of a theme issue on software engineering’s 50th anniversary.

Proposed technical service platform can assist in developing innovative services.Enterprises can extend company strength to obtain future technology leadership.This framework also provide feasible alternatives for future uncertainties. Developing a unique competitive advantage while simultaneously complying with future trends has emerged as a solid strategy for securing a leading technology production position in the global marketplace. Prior studies have suggested the development of a framework for analyzing technical service platforms and valid conjoint methodologies. A reliable technical service platform planning can generate a variety of services to provide customers with many options and choices. Meanwhile, concurrent planning can enhance customer satisfaction by integrating diversified products and technologies. A multi-step approach is proposed to organize technological service platforms while incorporating corporate strengths and technological forecasting during platform development. In addition, scenario analysis is conducted as a decision support system for top managers. Therefore, enterprises can allocate resources in strategic planning so as to advance in the future. A case study of three-dimensional integrated circuit (3D-IC) technological service platform planning in the semiconductor industry demonstrates the advantages of the proposed methodology. We evaluated six alternatives to ensure the compliance of our research with market demands. Results show our method can enable enterprises to address both existing and potential customer requirements, develop more innovative services that meet different market segments, and achieve economy of scale. We show how decision makers can determine feasible alternatives while using scenario analysis to offer solutions in the future under uncertainty. Using our method, company strengths can be extended well in the future through this integration of design and forecasting.

With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed combined models. The simulation results show that: (1) the autocorrelation function of the one-step ahead forecast errors of support vector machine shows more significant tailing than those of the autoregressive and persistence models and the correlation relationships of the multi-step ahead forecast errors of support vector machine do significantly exist, but in the case of the autoregressive and persistence models, they do not; (2) the proposed combined models have significantly enhanced the short-term wind power forecasting accuracy in the three forecasting modes. In particular, the one-step ahead forecasting accuracies of combined models show little difference; for the continuous multi-step ahead forecasting, the improvements of the proposed combined models compared with a certain individual model increase with increasing prediction steps.

Technology roadmap is one of the useful tools for managing R&D planning as well as identifying the future of technological progress at firms, various levels of government agencies, and other organizations. This study presents an industrial technology roadmapping process for use in public R&D programs, affecting firms' strategic behavior under this umbrella. The primary purpose of this paper is to address evolutionary aspects of industrial technology roadmap for the South Korea case and provide a more advanced framework of public R&D planning. Since 2000, technology roadmapping has been initiated by the government agency in South Korea, and, more recently, this practice has become popularized in small- and medium-sized companies as well. Despite the popularity, practitioners have some difficulties in finding practical guidelines and systematic processes for developing technology roadmaps applicable to R&D planning within an organization. The framework of industrial technology roadmapping developed by the Korea Institute for the Advancement of Technology can be applied or modified to the R&D planning process of similar projects. This roadmapping process deals with a variety of industries with different characteristics, and provides a systematic mechanism to predict future market demands and technology innovations. Finally, this paper shed some lights on the major challenges, potential roadblocks and proposed recommendations in roadmapping process.

Examines the future for cloud gaming services. These services have leveraged real-time video streaming technologies that have a long history of development. Cloud gaming refers to the technologies that offload parts of game software from traditional game consoles or personal computers (PCs) to powerful and elastic cloud infrastructures. Cloud gaming makes perfect sense to: 1) gamers, who otherwise have to constantly upgrade their consoles or PCs, which is certainly no fun and costly; 2) cloud service providers, who can sell the already-deployed and idling cloud resources to support the cutting-edge games that are extremely resource hungry; and 3) game developers, who no longer need to spend months to port their games to different platforms. As such, cloud gaming has attracted significant attention from both academia and industry.

Autoregressive time series forecasting is common in different areas within water resources, which include hydrology, ecology, and the environment. Simple forecasting models such as linear regression have the advantage of fast runtime, which is attractive for real-time forecasting. However, their forecasting performance might not be acceptable when a non-linear relationship exists between model inputs and output, which necessitates the use of more sophisticated forecasting models such as artificial neural networks. This study investigates the performance and potential of a hybrid pre-processing technique to enhance the forecasting accuracy of two commonly used neural network models (feed-forward and layered recurrent neural network models) and a multiple linear regression model. The hybrid technique is a combination of significant input variable selection (using partial linear correlation) for reducing the dimensionality of the input data and input data transformation using discrete wavelet transform for decomposing the input time series into low and high frequency components. Two case study forecasting applications, namely, monthly inflow forecasting for a lake in Victoria (Australia) and weekly algal bloom predictions at a bay in Hong Kong were used to assess the forecasting ability of the models when used in conjunction with the hybrid technique. Results demonstrated that the hybrid technique can significantly improve the forecasting performance of all the models used.

Although combining forecasts is well-known to be an effective approach to improving forecast accuracy, the literature and case studies on combining electric load forecasts are relatively limited. In this paper, we investigate the performance of combining so-called sister load forecasts, i.e. predictions generated from a family of models which share similar model structure but are built based on different variable selection processes. We consider 11 combination algorithms (three variants of arithmetic averaging, four regression based, one performance based method and three forecasting techniques used in the machine learning literature) and two selection schemes. Through comprehensive analysis of two case studies developed from public data (Global Energy Forecasting Competition 2014 and ISO New England), we demonstrate that combing sister forecasts outperforms the benchmark methods significantly in terms of forecasting accuracy measured by Mean Absolute Percentage Error. With the power to improve accuracy of individual forecasts and the advantage of easy generation, combining sister load forecasts has a high academic and practical value for researchers and practitioners alike.

Plug-in electric vehicles (PEVs) are not currently being sold in the United States at rates sufficient to meet the stated goals of vehicle manufacturers or the federal government. Although touted as able to help mitigate a range of public problems—including climate change, oil insecurity, and urban air pollution—PEVs face numerous barriers to commercialization. Research and development activities are under way that may overcome some of the key disadvantages of the current generation of PEVs. This analysis employs a survey-based discrete-choice exercise with 961 potential new vehicle purchasers in large US cities to assess how consumer demand might change with various breakthroughs in PEV technology. Respondents are presented with different price and technology scenarios and are asked to choose which of four powertrains they are most likely to purchase: a gasoline vehicle, a conventional hybrid, a plug-in hybrid electric vehicle (PHEV), or a battery electric vehicle (BEV). A multinomial logit is us...

Purpose – An increasing amount of attention is being paid to three-dimensional (3D) printing technology. The technology itself is based on diverse technologies such as laser beams and materials. Hence, 3D printing technology is a converging technology that produces 3D objects using a 3D printer. To become technologically competitive, many companies and nations are developing technologies for 3D printing. So to know its technological evolution is meaningful for developing 3D printing in the future. The paper aims to discuss these issues. Design/methodology/approach – To get technological competitiveness of 3D printing, the authors should know the most important and essential technology for 3D printing. An understanding of the technological evolution of 3D printing is needed to forecast its future technologies and build the R & D planning needed for 3D printing. In this paper, the authors propose a methodology to analyze the technological evolution of 3D printing. The authors analyze entire patent documents related to 3D printing to construct a technological evolution model. The authors use the statistical methods such as time series regression, association analysis based on graph theory, and principal component analysis for patent analysis of 3D printing technology. Findings – Using the proposed methodology, the authors show the technological analysis results of 3D printing and predict its future aspects. Though many and diverse technologies are developed and involved in 3D printing, the authors know only a few technologies take lead the technological evolution of 3D printing. In this paper, the authors find this evolution of technology management for 3D printing. Practical implications – If not all, most people would agree that 3D printing technology is one of the leading technologies to improve the quality of life. So, many companies have developed a number of technologies if they were related to 3D printing. But, most of them have not been considered practical. These were not effective research and development for 3D printing technology. In the study, the authors serve a methodology to select the specific technologies for practical used of 3D printing. Originality/value – Diverse predictions for 3D printing technology have been introduced in many academic and industrial fields. Most of them were made by subjective approaches depended on the knowledge and experience of the experts concerning 3D printing technology. So, they could be fluctuated according to the congregated expert groups, and be unstable for efficient R & D planning. To solve this problem, the authors study on more objective approach to predict the future state of 3D printing by analyzing the patent data of the developed results so far achieved. The contribution of this research is to take a new departure for understanding 3D printing technology using objective and quantitative methods.

The purpose of this paper is to study the evolution of emerging technological paths in the automotive industry. Worldwide patent data from the years 1990-2010 was collected and utilised to define the technological life cycles of the electric and fuel cell vehicle technologies. The novelty of our study is practicing the patent analysis approach using text mining techniques to collect patents according to their concepts in the automotive industry. The patent analysis results are compared to existing literature and expert opinion studies in alternative fuel vehicles field. The findings suggest that the development of electric vehicles will be quicker with a higher R&D share, compared to hydrogen vehicles. By gathering data and insights, the paper also offers general views on future automotive technology trajectories.

Technology roadmapping has an established and proven track record for helping organizations with their strategy, long-term planning, innovation and foresight activities. Technology Forecasting & Social Change has played a leading role in disseminating research on roadmapping and, so, for its 50th anniversary issue, this paper provides a historical account of the emergence of roadmapping from the practices developed and deployed by technological-based organizations. Motorola, BP, Philips, EIRMA, Lucent Technologies and the Semiconductor Industry Association have all made significant contributions leading to the modern form that embodies a temporal, multi-layered, systems-based approach underpinned by the ‘market-product-technology’ structure (i.e. why-what-how). However, the industrial roots of technology roadmapping can be traced back to an earlier period, which is, as of yet, unacknowledged in the technology and innovation management literature. There is the overlooked or, perhaps, forgotten history where organizations such as NASA, Boeing, GE, Lockheed, USAF, Rockwell International and the U.S. Department of Energy initiated and advanced the practice of roadmapping – through this publication, their contributions will now be given the credit they so rightfully deserve.

One of the most important issues in education has to do with the updating process of curriculum. Many social and political reforms try to take steps in order to improve the educational system, but rarely do they tackle with one of the most important problems that education has nowadays: the increasingly fast obsolescence of the school curricula. This concern is especially vital in fields that are directly related with technologies, like in different university degrees in engineering and subjects like technology in secondary school.Our research group has been facing this problem for the las two years introducing a new technique in the subject “Educational innovation and research”. The subject belongs to the MAES (University Master by the university of Seville for Secondary School Teachers) in the technology and industrial processes speciality. There is a quite wide bibliography about technological forecasting applied to different areas like marketing, product design, etc. However, is not easy to find cases of study where this technique is applied to educational curriculum designing directly, or, if so, these cases are not sufficiently documented and published.Nevertheless, fortunately there are some authors, such as Dimitry Kucharavy, that have carried out many studies and even in this case he has a specific subject about technology forecasting at the Politecnico di Milano [1]. Thus, we can extrapolate some procedures and theories to our students who are the people that have the important task of designing the curricula for the subject of technology in the Spanish secondary education for the near future.Technological forecasting techniques allow our students to face the curriculum designing tasks with an extraordinary and skilful orientation. Furthermore, they give them an additional motivation in their work.The aim of this paper is to describe the experience when introducing this technique in the last two academic years and discus the obtained results, as well as introducing the future prospects and the way forward in light of the speedy changes on technologiesKeywords: Technological forecasting, innovation, curriculum design

Each Industrial Revolution played a key role in the production-consumption pattern of textile products. Given the complexity of the Textile and Apparel Industry, the production systems have varying degrees of conceptual and technological innovation. Thus, the objective of this research is to present a model that includes the projection of technological changes in the production and consumption of textiles from the contrast between the descriptions of patents, scientific articles, and trends and tools of the Industry 4.0. It was possible to infer that the components of the Textile and Apparel Industry are undergoing an evolutionary process, which includes the automation of machinery parts and processes. The technological tools that characterize the Industry 4.0 are not yet fully integrated into textile production, and the challenge for the textile industry is to keep up with these innovations that impact on production, on business models, on consumer habits, and on social aspects. Keywords: textile and apparel industry, Industry 4.0, assessment, forecasting.

The article is devoted to the development of didactic bases for designing the learning process in professional educational institutions. From this standpoint, the authors propose the use of the prognostic approach, the didactic forecasting. The future state of the pedagogical process is determined on the basis of socioeconomic, scientific, technical, cultural, technological forecasting. A characteristic feature of the didactic design of the learning process in a professional educational institution is the presence of two components of the learning process: theoretical and industrial training, which necessitates the design of theoretical and production training. Designing theoretical training is associated with the development of modular programs of general educational, general technical and special subjects, pedagogical technologies that ensure the implementation of innovative approaches to the development of educational and cognitive activities. The design of production training is associated with the design of the production process, the material and technical and socio-technical environment. The design of the material and technical environment is associated with the equipment of study rooms, training workshops, with the definition of quantitative and qualitative characteristics of the raw materials, with technical and technological capabilities for manufacturing products. The design of the social and industrial sphere is associated with production, economic relations, and professional communication. Also, the article discusses the features of designing the learning process in a professional school related to establishing links between theoretical and production training, pedagogical and production tasks, between professional knowledge and production activities.

This study attempts to develop a methodology that analyzes patent applications to identify future skills, in particular in the sector of information security, recently into the spotlight. Matching skill elements from the International Patent Classification (IPC) with skill units from job analysis, the study tries to track trends in the skills needs based on IPC time-pattern. It then verifies the validity of the outlook for future skills needs by addressing the situation through the use of patents. The research assesses the usability of patent information for this type of analysis. While this study is limited to the information security sector by using Korean patent information, it can be expanded in the future to other areas and patents in the United States and Europe.

Our goal is to get better understanding of different kind of dependencies behind the high-level capability areas. The models are suitable for investigating present state capabilities or future developments of capabilities in the context of technology forecasting. Three levels are necessary for a model describing effects of technologies on military capabilities. These levels are capability areas, systems and technologies. The contribution of this paper is to present one possible model for interdependencies between technologies. Modelling interdependencies between technologies is the last building block in constructing a quantitative model for technological forecasting including necessary levels of abstraction. This study supplements our previous research and as a result we present a model for the whole process of capability modelling. As in our earlier studies, capability is defined as the probability of a successful task or operation or proper functioning of a system. In order to obtain numerical data to demonstrate our model, we conducted a questionnaire to a group of defence technology researchers where interdependencies between seven representative technologies were inquired. Because of a small number of participants in questionnaires and general uncertainties concerning subjective evaluations, only rough conclusions can be made from the numerical results.

Wind power forecasting (WPF) brings about decision risk to power system operation, because of its certain deviations. The uncertainty analysis of wind power forecasting (WPFUA) or wind power probabilistic forecasting (WPPF) provides probabilistic and confidence levels for decision makers to increase the forecasting certainty. In order to deeply and comprehensively understand WPFUA or WPPF model and then to optimize the forecasting model, the uncertain factors are firstly introduced in this paper. Secondly, the current WPFUA and WPPF methods are classified in terms of model input, modeling principle, express way and forecasting horizons. It summarizes the characteristics, merits and demerits, and application conditions of different methods. Thirdly, two advanced optimization strategies are discussed respectively from temporal and spatial perspectives. Finally, the development direction of uncertainty analysis technology in future is summarized. The objective of this review is, on one hand, to help WPPF users understand the model characteristics from different perspectives and select the most suitable one for various applications. On the other hand, it helps WPPF providers learn and optimize WPPF according to different characteristics.

With much more higher requirement for the precision of flood forecasting and length of forecast period in the hydrological operational predication, three coupling forecast methods which include real-time correction—combination forecast (RC-CF) method, combination forecast—real-time correction (CF-RC) method, and Integral Parameters Optimization (IPO) method are proposed in this paper for the purpose of improving the precision of flood forecasting. These coupling forecast methods are based on the real-time correction and combination forecast methods. Thereafter, two methods (method A & method B) are proposed for the purpose of prolonging the forecast period. Furthermore, indices of accuracy assessment which consist of mean absolute error, mean relative error, certainty coefficient and root-mean-square error are utilized to evaluate the forecast results of coupling forecast methods. Moreover, with a case study of Xiangjiaba station in the Jinsha River, advantages and disadvantages of these coupling forecast methods are obtained through the comparison of forecast results calculated by these methods, and they provide the basis for selection of coupling forecast methods. The result shows that the IPO method performs better than other two methods which behave undifferentiated. It is found that the IPO method combined with method B can be a viable alternative for flood forecasting of multiple hydrological models.

Technology forecasting estimates the future value of characteristics and performance of a technology. Since technologies are embedded in products, different measures of these products can be used in technology forecasting. Two classes of data play a central role in technology forecasting studies. In the first class, publications and patents are commonly excepted measures as indicators of scientific and technological performance. The second class is the performance data of the “technology in use”. In this second type of data, performance is usually characterized by multiple parameters in a complex product system since these product systems are the aggregates of subsystems. Technological progress obtained by these two types of datasets may show different patterns. Also each parameter of the overall system may show different pattern either. Authors claim that, in order to improve the quality of technology forecasts, both datasets should be studied. A multidimensional technology life cycle should be considered before taking managerial decisions. In this study an application of a refrigerator system has been performed to investigate the authors’ claim. Three types of datasets, patents from first type of dataset; coefficient of performance (COP) and electric efficiency index (EEI) from second type of dataset are used. Different life cycles and different scenarios of the same system are obtained using growth curves as a technology forecasting tool. Findings are discussed and the proposed model of using measures in technology forecasting is explained in detail.

We all see the increasing electrification of everyday things around us, from hybrid-electric and all-electric cars to the handheld devices we carry. What few people outside of the industry realize is that there has been a similar electrification occurring at sea on merchant ships, offshore platforms, and warships. Unlike cars and airplanes, few people use ships today, with the exception of the people who take vacations on cruise ships. This article attempts to provide both a look at the past and a glimpse into the future of shipboard power and propulsion systems, an arena that has dynamically changed and will continue to change in the coming decades.

The goal of this paper is to present the ranking of the journals in the field of technology forecasting (TF) through the application of the Data Envelopment Analysis - DEA. Over the past four decades, we have witnessed a rapid development in the field of technology forecasting. This development, both in theory and in practice, has been fuelled by the increase in complexity, even greater competition, and rapid changes in the business environment. Back in the past, little attention was paid to the importance and conceptual development in the field of TF, which caused numerous overlaps of the forms of TF development and its influence. The changes that have resulted in the emergence of information technology and modern manufacturing technology have actually increased the need for application of technology forecasting, as well as for explosive growth in scientific and engineering literature worldwide. The paper shows the ranking of 39 journals in the field of TF over the period from 1999 to 2011 through the application of the Data Envelopment Analysis, as well as through analyzing trend changes in publishing professional publications in the field of technology forecasting. Also pointed out are the shortcomings ofprevious methods of ranking journals by using impact factors, as one of the most important indicators of the quality of journals, as well as the possibilities of applying indicators obtained by using other methodologies. The results of such method of ranking should point out to publishing trends to the researchers engaged in this field, and direct them to journals in which they could present the results of their research. The aim of this research is to show that more than one criterion must be taken in order to create the rank of a journal from a specific area. In this way we get a more realistic ranking of leading journals from the observed area, since other necessary elements that contribute to the importance of a journal for a relevant field are also taken into consideration.

Technology forecasting is about understanding a status of a specific technology in the future, based on the cur-rent data of the technology. It is useful when planning technology management strategies. These days, it is com-mon for countries, companies, and researchers to establish R&D directions and strategies by utilizing experts’ opinions. However, this qualitative method of technology foreca sting is costly and time consuming since it re-quires to collect a variety of opinions and analysis from many experts. In order to deal with these limitations, quantitative method of technology forecasting is being studied to secure objective forecast result and help R&D decision making process. This paper suggests a methodology of t echnology forecasting based on quantitative analysis. The methodology consists of data collection, principa l component analysis, and technology forecasting by logistic regression, which is one of the data mining techniq ues. In this research, patent documents related toautonomous vehicle are collected. Then, the texts from patent d ocuments are extracted by text mining technique to construct an appropriate form for analysis. After principal component analysis, logistic regression is performedby using principal component score. On the basis of this result , it is possible to analyze R&D development sit-uation and technology forecasting. Key Words : Technology Forecasting, Patent, Logistic Regression, Autonoum ous vehicle, Principal component analysisReceived: Sep. 14, 2014Revised : Sep. 28, 2014Accepted: Jan. 20, 2015

The rapid advancement of the global optical disc industry results from systematic changes during the product life cycles of digital audio-visual technology. Compact discs (CD) and Digital Versatile Disc (DVD) sales reached record highs in 2004 and 2006 respectively, and the markets continue to shrink as CDs, DVDs, and related consumer electronic products have entered the mature stage of development following the entry of Blu-ray DiscTM (BD) products. This research explores the history, evolution, and systematic innovation driven by consumer demands for these products. The research provides a life cycle analysis of technology and market trends for the optical disc industry. Logistic curves are used to model the patent statistics and market values of DVD and BD technologies to document data trends from the mid-1990s till now. The results show that BD technology patent filings rapidly reached the decline point two years after DVD technology matured. The results also show that systematic innovation of optical...

 Abstract— examine the influencing factors associated with the use of unmanned aerial system (UAS) technology to support aviation accident and emergency response. The ability of first responders to react to an emergency is dependent on the quality, accuracy, timeliness, and usability of information. With aviation accidents such as the Asiana Airlines Flight 214 crash at San Francisco International Airport, the ability to sense and communicate the location of victims may reduce the potential for accidental passenger death. Furthermore, the ability to obtain information enroute to an accident may also to assist to reduce overall response and coordination time of first responders (e.g., Aviation Rescue and Firefighting [ARFF]). By identifying and examining current and potential practices, capabilities, and technology (e.g., human-machine-interface [HMI], human factors, tools, and capability modifiers) a more comprehensive model of the influencing factors is established to further support the growing body of knowledge (i.e., safety, human computer interaction, human-robot systems, socio-economical systems, service and public sector systems, and technological forecasting). A series of recommendations regarding the technology and application are provided to support future development or adaptation of regulations, policies, or future research. Index Terms—unmanned aerial systems, UAS emergency response, UAS aviation accident response, UAS application, UAS HMI, UAS disaster response, UAS situational awareness

In this paper, we forecast industrial production growth for the Turkish economy using static factor models. We evaluate how the performance of the models change based on the number of factors we extract from our data as well as the level of aggregation for the series in the data set. We consider two evaluation samples for the out-of-sample forecasting exercise to assess the stability of the forecasting performance. We find that the effect of the data set size on the forecasting performance is not independent from the number of factors extracted from this data set. Rankings of the models change in different evaluation samples. We conclude that using a dynamic approach to evaluate models from different dimensions is important in the forecasting process.

The paper presents an overview of the FORMAT methodology, an approach for technological forecasting capable of satisfying the need of manufacturing industries to better support decision-making processes in planning corporate strategies for R&D. The methodology has been developed within the FORMAT consortium, gathering partners from both industry and academia in a Marie-Curie EU funded project of the 7th Framework Programme - IAPP PEOPLE. The methodology is intentionally shaped as a generic one, so as to foster its adoption by the wider audience, but it suggests the adoption of several TRIZ techniques and models to support the forecast. Its exemplary application in the field of forming technologies for domestic refrigerators aims at showing its potential and viability in a real industrial context. The paper is supplemented by a case study in the white goods manufacturing (forming techniques for the inner liners of domestic refrigerators).

This paper describes the first outcomes of a joined research project between Politecnico di Milano and Whirlpool Europe aimed at establishing a systematic industrial approach to anticipate technological demands and solutions to manufacture future household appliances, by taking into account emergence, performances and drawbacks of manufacturing technologies. This work adds new elements of analysis to the forecasting approach proposed by the authors in previous publications and updates its metric for classifying Evaluation Parameters with a detailed set of criteria to characterize the outcomes, the drawbacks and the main resources consumption of a generic industrial process. Such set allows a preliminary extraction of information from technical experts, with no specific TRIZ know- how, in order to ease the process of identifying the main barriers (in TRIZ terms, contradictions) limiting the development of current technologies and/or their substitution with new industrial processes. The original content of the paper is clarified by means of a case study related to an application of the methodology concerning the foaming process of domestic refrigerators.

Thermal spraying processes allows high cooling rates and can favor the formation of amorphous microstructures. Amorphous metallic coatings can result in superior mechanical and functional properties. To follow new technological development and innovation paths, indicators based on patent data can be assembled to support technological forecasting assessments and decision making. This study mapped the technological development on amorphous alloys processed by thermal spraying using patent indicators from documents indexed in the Derwent Innovations Index database between the years 1997 and 2014. We evaluate the patent activity, the role of countries, the main technological subdomains and markets of interest, as well as the main metallic alloys explored as coating. We conclude that new technological developments should be expected in near future and these advances both in amorphous alloys and thermal spraying shall be constantly monitored in the coming years.

Shifts in technology can bring dramatic changes in the competitive positioning of an organization. On the one hand it provides opportunities to create new value propositions and thus opportunity to grow, but on the other hand it also threatens the existence of organizations that fail to grasp the scale and/or significance of given technological change. Technology intelligence and forecasting have long been used to evaluate and forecast the potential characteristic, rate and effects of technological change. In this paper, we argue for the position that unstructured text provides a rich source of data that can be used for forecasting emerging technologies. A framework is developed to leverage the capabilities of text anaytics techniques to augment methods used for technology forecasting.

Understanding the nature of the diffusion process is crucial for sustainable development of a new technology and product. This study introduces a replacement diffusion model that leads to a better understanding of the growth dynamics of a technology. The model operates in an environment with multiple competitors and overcomes the limitations of existing models. The model (1) consists of a diffusion model and an additional time series model; (2) separately identifies the diffusion of first-time purchases and that of replacement purchases; (3) incorporates players’ marketing-mix variables, affecting a new technology diffusion; and (4) characterizes consumers’ different replacement cycles. The proposed model is applied to South Korea’s mobile handset market. The model performs well in terms of its fit and forecasting capability when compared with other diffusion models incorporating replacement and repeat purchases. The usefulness of the model stems from its ability to describe complicated environments and its flexibility in including multiple factors that drives diffusion in the regression analysis.

We use both TFDEA and Regression to forecast the diffusion of battery electric cars. Our methodology was divided into 6 steps. The objective of this project is to compare and discuss the results of TFDEA and Regression to be used to predict the model year of Battery Electric Vehicle. For project scope, the dataset of BEV will only focus on the BEV sold or available in US market from 1997∼2015. The selection of variables will concentrate on the technical performance parameters in 2 seats and 4 seats market segments respectively.