Technical Roadmaps Research Articles

An intelligent garment for long COVID-19 real-time monitoring

As monitoring and diagnostic tools for long COVID-19 cases, wearable systems and supervised learning-based medical image analysis have proven to be useful. Current research on these two technical roadmaps has various drawbacks, despite their respective benefits. Wearable systems allow only the real-time monitoring of physiological parameters (heart rate, temperature, blood oxygen saturation, or SpO2). Therefore, they are unable to conduct in-depth investigations or differentiate COVID-19 from other illnesses that share similar symptoms. Medical image analysis using supervised learning-based models can be used to conduct in-depth analyses and provide precise diagnostic decision support. However, these methods are rarely used for real-time monitoring. In this regard, we present an intelligent garment combining the precision of supervised learning-based models with real-time monitoring capabilities of wearable systems. Given the relevance of electrocardiogram (ECG) signals to long COVID-19 symptom severity, an explainable data fusion strategy based on multiple machine learning models uses heart rate, temperature, SpO2, and ECG signal analysis to accurately assess the patient's health status. Experiments show that the proposed intelligent garment achieves an accuracy of 97.5 %, outperforming most of the existing wearable systems. Furthermore, it was confirmed that the two physiological indicators most significantly affected by the presence of long COVID-19 were SpO2 and the ST intervals of ECG signals.

Numerical Simulations for in-Plane Distribution of Platinum Degradation in Dynamic Operating Conditions of Polymer Electrolyte Membrane Fuel Cells

Predicting degradation of polymer electrolyte membrane fuel cells (PEMFCs) is an important issue from the viewpoint of durability and cost. In fact, in technical roadmaps regarding fuel cell of each country, high targets of durability and performance are set for transport applications especially for heavy duty vehicles. One of important degradation process takes place in catalyst layer. PEMFC catalysts are typically platinum or platinum alloys and consist of nanoparticles to increase the electrochemically active surface area (ECSA). However, these particles are not stable in dynamic operating conditions of the transport applications. A typical degradation mechanism of the catalyst is the electrochemical Ostwald ripening mechanism, and it is known that the platinum particle size distribution shifts to larger particle diameter, resulting in lower ECSA and lower performance. Therefore, predicting platinum catalyst degradation is important to enhance durability of the products.Various numerical models for catalyst degradation of PEMFC have been proposed and used to explore degradation mechanisms. Many of these are zero or one-dimensional models, but factors that affect catalyst degradation, such as temperature, potential, and humidity, have non-uniform distributions in the active area of large-size (several hundred cm2) cells. Furthermore, these factors change unsteadily due to the dynamic operating conditions. In order to predict the degradation of cells, it is necessary to analyze the dynamic behavior of large-area cells, but there are few spatially resolved catalyst degradation predictions [1].In this analysis, transient analysis of a large-area cell for a dynamic operating conditions is performed to predict in-plane distribution of catalyst degradation. Furthermore, we will analyze changes in the power generation performance of the cells when these degradations progress. A three-dimensional numerical simulation model based on our original methodology [2] was used as the performance prediction model for the PEMFC. Catalyst degradation considers the mechanisms of platinum dissolution, oxidation, and platinum oxide dissolution [3]. The in-plane distribution of platinum degradation is predicted from the time-series results of in-plane distributions of temperature, potential, humidity, etc., obtained by the performance prediction model. The performance of the PEMFC at the time of progressive degradation is analyzed by reflecting the effect of ECSA decrease due to platinum degradation in the performance prediction model. Although this method considers only a part of the catalyst degradation process, a path is presented to analyze the distribution of catalyst degradation under dynamic operating conditions in large-area cells.

X-RCRNet: An explainable deep-learning network for COVID-19 detection using ECG beat signals

Wearable systems measuring human physiological indicators with integrated sensors and supervised learning-based medical image analysis (e.g. ECG, X-ray, CT or ultrasound images for lung or the chest) have been considered relevant tools for COVID-19 monitoring and diagnosis. However, these two technical roadmaps have their respective advantages and drawbacks. The current wearable systems enable to realize real-time monitoring of COVID-19 but are limited to its basic symptoms only, neither allowing to distinguish it from other diseases nor performing deep analysis. Current medical image analysis can provide accurate decision support for diagnosis but rarely deals with real-time data processing. In this context, we propose a new wearable system by combining the advantages of these two technical roadmaps. Considering that electrocardiogram (ECG) has been proved relevant to evolution of COVID-19 symptoms, the proposed wearable system will integrate an explainable Deep Neural Network to realize online monitoring of COVID-19 gravity by using ECG beat signal. This paper will focus on the Deep Neural Network model named X-RCRNet. The network is based on ResNet18 but with few enhancements: 1) LSTM Layers for regenerating the backpropagation error and further extracting the involved time-varying features; 2) LeakyReLU for increasing the performances of the model. With an accuracy of 96.48% after experiments, ourmodel has not only outperformed the existing methods in terms of accuracy and robustness, but also originally identify the ST interval of the ECG pattern,as the most prominent key features affected by the virus.

Simple is best: A single-CNN method for classifying remote sensing images

<abstract> <p>Recently, researchers have proposed a lot of methods to boost the performance of convolutional neural networks (CNNs) for classifying remote sensing images (RSI). However, the methods' performance improvements were insignificant, while time and hardware costs increased dramatically due to re-modeling. To tackle this problem, this study sought a simple, lightweight, yet more accurate solution for RSI semantic classification (RSI-SC). At first, we proposed a set of mathematical derivations to analyze and identify the best way among different technical roadmaps. Afterward, we selected a simple route that can significantly boost a single CNN's performance while maintaining simplicity and reducing costs in time and hardware. The proposed method, called RE-EfficientNet, only consists of a lightweight EfficientNet-B3 and a concise training algorithm named RE-CNN. The novelty of RE-EfficientNet and RE-CNN includes the following: First, EfficientNet-B3 employs transfer learning from ImageNet-1K and excludes any complicated re-modeling. It can adequately utilize the easily accessible pre-trained weights for time savings and avoid the pre-training effect being weakened due to re-modeling. Second, RE-CNN includes an effective combination of data augmentation (DA) transformations and two modified training tricks (TTs). It can alleviate the data distribution shift from DA-processed training sets and make the TTs more effective through modification according to the inherent nature of RSI. Extensive experimental results on two RSI sets prove that RE-EfficientNet can surpass all 30 cutting-edge methods published before 2023. It gives a remarkable improvement of 0.50% to 0.75% in overall accuracy (OA) and a 75% or more reduction in parameters. The ablation experiment also reveals that RE-CNN can improve CNN OA by 0.55% to 1.10%. All the results indicate that RE-EfficientNet is a simple, lightweight and more accurate solution for RSI-SC. In addition, we argue that the ideas proposed in this work about how to choose an appropriate model and training algorithm can help us find more efficient approaches in the future.</p> </abstract>

KAiPP: An interaction recommendation approach for knowledge aided intelligent process planning with reinforcement learning

Effective reuse of historical process knowledge has been one of the most important technical roadmaps driving traditional human experience-based process planning all the way to intelligent process planning in the context of Industry 4.0. However, effective knowledge reuse in process planning is still made difficult nowadays due to the lack of means to capture the technologists’ preference embedded in the interaction history of technologists with the system, and the mutual influence of process decision-making activities for future knowledge recommendations. To bridge the gap, this work takes in-depth integration of deep learning and reinforcement learning in process planning and proposes a novel dynamic interaction recommendation approach for knowledge aided intelligent process planning (KAiPP) towards Industry 4.0. This work first formalizes KAiPP as a sequential interaction knowledge recommendation scenario, to which a Markov decision process is employed to model the KAiPP scenario. On that basis, a knowledge-aware reinforcement learning model is designed to learn a KAiPP agent through interacting with the KAiPP environment for effective interaction knowledge recommendation in process planning. Here, the technologists’ preference-task information model and dynamic knowledge sub-graph are elaborated as the KAiPP environment to capture the preference of the technologist and mutual influence of process decision-making activities. Finally, experimental results show that the proposed approach achieves a very high recommendation performance that outperforms the state-of-the-art approaches in the process planning domain. In addition, a thus developed KAiPP prototype system provides a reference for industrial implementation of the approach.

Fuel economy improvement of a turbocharged gasoline SI engine through combining cooled EGR and high compression ratio

HEV (hybrid electric vehicles, HEV) is considered to be one of the most important automotive technical roadmaps. Fuel efficiency level of the engine used in HEV has significant impact on vehicle fuel consumption. This paper studies the performance and fuel economy potential of applying high GCR (geometrical compression ratio, GCR) and cooled EGR in turbocharged gasoline SI engines dedicated to the use in HEVs. A 1-D simulation model for a turbocharged EGR engine has been established based on a baseline 1.5T engine. Then the effects of cooled EGR and higher GCRs on engine combustion and performance have been studied and optimized. As increasing GCR from 9.7:1 to higher values that are 12.5:1 and 14:1, the full load torque decreases obviously with the maximal relative decrease of 7.6% and 21.9% respectively mainly because EGR is used to suppress the knock reducing the volumetric efficiency. The fuel economy in the most speed and load range is greatly improved mainly because of higher constant volume degree, combustion efficiency and less heat transfer and exhaust losses. In HEVs, engines mainly work in the efficient operating range, the torque reduction can be compensated by drive motor, and an appropriately high GCR is advantageous.

Targeted Muscle Reinnervation in Amputees: A Review of Current Techniques

Targeted muscle reinnervation (TMR) is a surgical technique that was initially developed for amputees to improve prosthetic control. TMR reroutes the ends of amputated nerves into more proximal motor nerves supplying residual redundant muscle groups. In doing so, it restores motor signals from the brain that would otherwise be lost by the amputation and allows the redundant muscle to communicate this lost function to a prosthetic device. Although initially developed for functional purposes, TMR has subsequently been shown to treat neuropathic and phantom limb pain. As such, indications for its use have significantly expanded. TMR is now widely performed for both upper and lower extremity amputees, at various amputee levels. While technical roadmaps have been published for many amputation levels, the goal of this paper is to update and summarize all these techniques in one place.

Thinking differently: lessons learned by international public health specialists while supporting the Integrated Disease Surveillance and Response system in Pakistan

Internationally supported activities to build public health capacity and improve compliance with International Health Regulations (2005) so that countries are better able to ‘prevent, protect against, control and provide a...

Development and dissemination of deep-energy retrofit strategies through a mandatory municipal building tune-up ordinance in Seattle, Washington, USA

This paper describes initial implementation of a municipal-scale building-owner engagement and technical assistance process aimed at accelerating voluntary deep energy retrofits as part of a new mandatory building tune-up requirement. Leveraging building energy consumption disclosure data, a municipal ordinance mandating building tune-ups at five-year intervals, and a suite of freely-available energy simulation tools, the university-based research and deployment team seeks to develop a scalable pathway for creating custom technical and financial roadmaps for deep-energy retrofits that drive carbon-neutral operations in existing medium-sized buildings.

MEMS-Based Vibrational Energy Harvesting and Conversion Employing Micro-/Nano-Magnetics

This paper discusses the current state-of-the-art, ongoing fundamental and technical challenges and potential roadmaps for micro-scale vibration energy harvesting and power conversion devices employing micro-/nano-magnetics. Such devices are of paramount importance as powering solution for autonomous and ubiquitous sensor nodes within the emerging “Internet of Things (IoT).” In this paper, we have reviewed the fundamental limitations, technological needs, and breakthroughs in the mentioned areas, including materials, process integration, and device design issues. Particularly, current limitations in both the relevant soft and hard (or permanent) micro-magnets, roadmaps for a complete “Magnetic MEMS” solution for energy harvesting and efficient low-power conversion for “IoT” applications, are discussed.

Tuning electrical conductivity, charge transport, and ferroelectricity in epitaxial BaTiO3 films by Nb-doping

The electrical conductivity, charge transport behavior, and ferroelectricity of epitaxial BaNbxTi1-xO3 films (BNTO, 0.0 ≤ x ≤ 0.5) prepared by pulsed laser deposition are investigated. It is found that Nb-doping can tune the conventional insulating BaTiO3 films from an insulating to highly conductive semiconducting or metallic state, resulting in a variation of the electrical conductivity of the BNTO films over 105. For x ≤ 0.25, the charge transport is dominated by the small polaron hopping mechanism, while the charge transport for x = 0.5 transits from the bipolaron to the small-polaron, and then the thermal phonon scattering mechanisms with increasing temperature. Interestingly, the piezo-force microscopy imaging reveals the presence of ferroelectricity in the properly Nb-doped conductive BNTO films (x ≤ 0.25) deposited in the presence of a small amount of oxygen (3 × 10−3 Pa). Our work provides additional technical roadmaps to manipulate the conductivity and charge transport behaviors in ferroelectric films, which will boost potential applications in future information storage, sensors, and photovoltaic devices.

既存ロードマップの予測方向と実際の方向との比較 - 交通・物流部門

The Transportation & Logistics division published many Technical Roadmaps since 2006. Almost 10 years past now and it seems good time to look back on the activity. 2D-ARM was developed to describe complicated system.

Research Issues in Smart Vehicles and Elderly Drivers: A Literature Review

This article reviewed both studies on general smart car technologies and human–computer interaction (HCI)/human–vehicle interaction studies that were published in journals and conferences so that the current status of research can be identified and future research directions can be suggested. Furthermore, previous studies on elderly drivers were reviewed, as these drivers could be the most vulnerable social group in terms of new technology acceptance. A total of 257 articles for HCI research and 45 articles for elderly drivers were selected and reviewed from 11,267 collected articles (2010–2014). According to the results, most articles were mainly related to safety and adaptive features (e.g., driver’s state recognition, vehicle surrounding monitoring, driver action-suggestion), and infotainment research in terms of HCI (e.g., information technology devices–vehicle interaction, vehicle–vehicle interaction) was relatively insufficient despite its high research demand. According to the results of the literature review and technological trends analysis based on previous technical road maps, from HCI/human factors engineering (HFE) perspectives, research related to “Assistance systems,” “Physiological & mental state recognition,” “Position sensor technology,” “Behavior recognition,” and “Infotainment” was suggested to HCI/HFE researchers for further research. In particular, HCI/HFE researchers need to focus on research on acceptable levels of automation, observing new driving behaviors, investigation of driver characteristics to develop personalized services, and new technology acceptance to develop and improve smart cars in the future.

P002001 技術ロードマップから見る2030年の社会

P002001 技術ロードマップから見る2030年の社会

Research on Basic Standard of Roadmap for Green Manufacturing

The technical roadmap which has been produced since the 1980s is used in many fields. In reality it has formed many technical roadmaps standard. Summarizing and studying these standards has the important meaning to this emerging modern management tool's development consummation of the technical road map. This article has carried on the preliminary discussion from the technical roadmap content, the formulation procedure, the representation, the formulation method and so on.

P00100 技術ロードマップから見る2030年の社会 : ロボットと自動車から見た機械工学全体の将来展望(特別企画,イノベーションセンタ)

P00100 技術ロードマップから見る2030年の社会 : ロボットと自動車から見た機械工学全体の将来展望(特別企画,イノベーションセンタ)

How to “Capture the Future by Utilization of the Past” in the Coming Revision of China CO2 Technology Roadmap?

A technology roadmap is the document that is generated by the technology road-mapping process. It usually identifies the critical system requirements, the product and process performance targets, and the technology alternatives and milestones for meeting those targets. In effect, a technology roadmap identifies alternate technology “roads” for meeting certain performance objectives. CCUS has great potential to reduce CO2 emissions substantially from the whole lifecycle of traditional energy-intensive industry at a large-scale. In this paper, we do not want to address how to generally make a CO2 technology roadmap. We are concerning about the gaps and suggestions which should be closed in the coming revision of China CO2 technology roadmap 2011. The paper presents a summary of latest situations of international and national CCUS technical roadmaps. Then, the challenges existed in the past and also emerged currently in China are proposed. Finally, some comments and suggestions for the coming version of China's CCUS technology roadmap are displayed to guide ongoing development of CCUS in China based on the development situation and challenges of China CCUS RD&D in the past and at the present stage.

Theoretical framework and application of computer-aided experimental human geography： Case study of urban residential location choice

This paper describes the concept of computer-aided experimental human geography,which has been proposed as a methodology in geography. Experimental human geography is defined as a geographical methodology to reproduce human geographical phenomenon experimentally by means of computer sciences and GIS technologies. Its academic function is to study human-environment relationship under explicit geographic structure,implicit characteristics of human behavior,and explicit and implicit environment information. Accordingly,the paper has discussed the scope,principles and technical roadmaps of experimental human geography. In comparison to experimental economics,the paper has found that,in principle,many methods and rules of experimental economics are still effective in experimental human geography,but experimental human geography is more concerned about approaching natural processes simulation of geographic environment. Therefore,certain pre-designed models and special algorithms are necessary for experimental human geography. The experimental human geography rules include "value-induced" principle,"response-game" principle,"experimental control" principle,"statistics-based conclusion" principle,"geographical approximation" principle,and "experimental packaging" principle. Using the methodology of computer-aided experimental human geography,this paper studies the urban residential location choices,with two study objectives:(1) When the layout of urban infrastructure changes,what factors affect the dependence of the subjects on the city center,the reasons for destroying this dependence.(2)Whether the two different urban spatial structures,Burgess Model case and Hoyt Model case,would affect differently when the subjects were asked to choose a residential location. Based on the experiment objectives,the author made five experiment assumptions: urban space layout,residential location,residential location price,job location,incentive mechanisms. And the results are as follows:(1) Differences in urban residential space structures didn't have significant impact on residential location choices of the subjects.(2) In the case of the two typical urban spatial structures,the subjects were more concerned about traffic condition and school district around a residential location when asked to choose.(3) The price factor was not an obvious impact on the residential location choices,which might be the reason that not too many subjects accumulate in a particular area. This case study has shown that the thought and means of computer-aided experimental human geography are feasible. In other words,it is feasible to study the characteristics of human behaviors,such as decision-making behavior,in a controlled experimental environment with clear and realistic geographical structures and designed experimental contents based on GIS and computer technology. In short,this paper tries to develop a basic scientific framework for computer-aided experimental human geography,and much more remains to be done in the future.

협대역 및 광대역 PPDR 서비스 제공을 위한 재난통신망의 효율적인 구축 방안 연구

본 논문은 국내외 재난통신망 구축 현황과 기술 발전을 조사 분석하여 합리적이고 효율적인 재난통신망 구축 방안을 제시한다. 연구한 결과, 우리나라는 TRS 기반의 재난통신망이 전국 규모로 구축되어 있지 못한 상황에서 완전 재난통신 광대역 자가망으로 진화하기 위해서는 5년 이상의 과도기적인 재난통신망 구축 및 운영이 불가피하다. 따라서 광대역 이동 통신 기술 기반의 자가망을 구축하면서 이 기술이 성숙될 때까지 상용 TRS와 상용 모바일 광대역망을 보완 활용하는 것이 필요하며, 이를 구체적으로 설계하고 검증하기 위한 전담 조직의 설립이 적극적으로 검토될 필요가 있다. In this paper, reasonable and effective building strategies of PPDR network is presented through investigating and analysing current policies for PPDR network of Korea and foreign countries. The research result shows that it is necessary PPDR network is designed and implemented considering currently transient status in aspects of PPDR-related technical roadmaps and existing PPDR networks. Also, in the transient period of more than 5 years, mobile broadband- based private PPDR network should be builded and it should be complemented by some commercial networks such as TRS or other mobile broadband networks.

The Godson Processors: Its Research, Development, and Contributions

The Godson project with an R&D history of 10 years is an independent national program of China that aims at developing advanced microprocessor technologies based on fundamental research and commercialization of the chip technology. We will give a comprehensive presentation of the Godson project, including its history, technical roadmaps, and several unique technical merits.