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We are pleased to announce that the 16th Journal of Robotics and Mechatronics Best Paper Award (JRM Best Paper Award 2023) has been decided by the JRM editorial committee. The following paper won the JRM Best Paper Award 2023, severely selected from among all 113 papers published in Vol.34 (2022). The Best Paper Award ceremony was held on December 26, 2023 in hybrid style (both on-site and online; venue: Gakushi-Kaikan, Tokyo, Japan), attended by the authors and JRM editorial committee members who took part in the selection process. The award winners were given certificates and a JPY100,000 honorarium. Editorial committee members who participated online also congratulated them through Zoom. We congratulate the winners and sincerely wish them success in the future. Teppei Tsujita, Yuto Inoue, Yutaka Takagi, Atsushi Konno, Satoko Abiko, Xin Jiang, Atsuhiro Nakagawa, and Masaru Uchiyama Development of Haptic Interface for Neurosurgical Simulators with Micro Scissors Module for Displaying the Cutting Force Journal of Robotics and Mechatronics, Vol.34, No.6, pp. 1284-1296, December 2022 [ABSTRACT FROM AUTHOR]

At the present stage, the identification of dangerous behaviors in public places mostly relies on manual work, which is subjective and has low identification efficiency. This paper proposes an automatic identification method for dangerous behaviors in public places, which analyzes group behavior and speech emotion through deep learning network and then performs multimodal information fusion. Based on the fusion results, people can judge the emotional atmosphere of the crowd, make early warning, and alarm for possible dangerous behaviors. Experiments show that the algorithm adopted in this paper can accurately identify dangerous behaviors and has great application value. [ABSTRACT FROM AUTHOR]

This paper studies the cooperative multi-area coverage problem with obstacles, which requires a group of robots to cover an area while avoiding collisions. This problem is very common in scenarios such as garbage removal, mine clearance, and regional information collection. The currently proposed algorithms usually have the problem of high redundancy and weak scene scalability. This paper designs a cooperative area coverage algorithm for multiple robots. First, a set of rules is proposed to divide the area to be covered into several small areas. Then, a genetic algorithm based on circular coding is designed to allocate these divided areas to several robots. Finally, the coverage path of the robot is designed using the zigzag method, so that the robot can cover the area allocated to it. Through computational experiments, it has been verified that this algorithm has efficiency advantages over state-of-the-art algorithms in certain scenarios and has scalability for different scenarios. [ABSTRACT FROM AUTHOR]

Using the improved Johnson et al.'s style migration network as a starting point, this paper proposes a new loss function based on the position information Gram matrix. The new method adds the chunked Gram matrix with position information, and simultaneously, the structural similarity between the style map and the resultant image is added to the style training. The style position information is given to the resultant image, and finally, the resolution of the resultant image is improved with the SRGAN. The new model can effectively migrate the texture structure as well as the color space of the style image, while the data of the content image are kept intact. The simulation results reveal that the image processing results of the new model improve those of the classical Johnson et al.'s method, Google Brain team method, and CCPL method, and the SSIM values of the resulting map and style image are all greater than 0.3. As a comparison, the SSIM values of Johnson et al., Google Brain team, and CCPL are 0.14, 0.11, and 0.12, respectively, which is an obvious improvement. Moreover, with deeper training, the new method can improve the similarity of certain resulting images and style images to more than 0.37256. In addition, training other arbitrary content images on the basis of the trained model can quickly yield satisfactory results. [ABSTRACT FROM AUTHOR]

Integrated energy microgrids and shared energy storage have significant benefits in improving the energy utilization of the system, which is gradually becoming the current research hotspot. And the uncertainty of new energy output also significantly affects the stable and economic operation of integrated energy microgrid. So how to establish a set of integrated energy microgrids optimization operation model considering photovoltaic (PV) output uncertainty and shared energy storage is an urgent problem to be solved nowadays. Firstly, this paper introduces the framework of an integrated energy system microgrid containing a shared energy storage operator (ESO), and analyzes the scheduling method of the upper tier operator within the system as well as the economic benefits at the lower tier user end. Secondly, to address the randomness of PV output, Monte Carlo method is used to generate the scenarios, and then the scenarios are cut down by using the fast antecedent elimination technique. Then, an optimal operation model is established for micro grid operator (MGO) and user aggregator (UA), respectively, and based on the master-slave game relationship, so that the MGO is the leader and the UA is the follower, a Stackelberg game model is proposed to consider the integrated demand response of electricity and heat between the MGO and UA in the context of the participation of ESO in the auxiliary service of the UA. Finally, the proposed model is brought into a typical residential building community for simulation verification, and the results show that the model proposed in this paper can effectively balance the interests of MGOs and UAs, and realize win-win benefits for UA and ESO. [ABSTRACT FROM AUTHOR]

Unbalanced and insufficient development is a prominent problem in China's period of economic transformation, and an accurate grasp of the current situation and evolutionary trend of innovation and economic development is of great significance in finding a solution and promoting regional coordinated development. This paper introduces the framework of functional data analysis to the study of both innovation and economic development in China, examining its spatial dynamic distribution and convergence characteristic in greater depth. Empirical results revealed the following. (1) Significant regional differences exist in the absolute level of China's innovation development, with a balanced improvement in the development speed and growth potential. Significant differences exist in the absolute level and speed of economic development, but not in the potential energy of development as reflected by acceleration. (2) The σ(t) function of nationwide and regional innovation development shows a downward trend. There is σ(t) convergence in economic development nationwide and in the northeastern, eastern, and western regions, but the regional disparities in economic development within the six central provinces have not yet been effectively mitigated. (3) Nationwide and regional innovation development are in a state of β(t) convergence. There is significant β(t) convergence in the nationwide economic development, with a transition point from divergence to convergence in the northeastern, eastern, and western regions. However, the β(t) function of economic development in the central region shows a region-wide divergence. The findings of this paper have important policy implications for recognizing the balance and disparities in innovation and economic development among regions in China and promoting coordinated regional development. [ABSTRACT FROM AUTHOR]

Considering that social network provides a channel for nodes to exchange information, resources, and interests, the fundamental task of social network search is to find the best path from the source node to the target node. The search strategy based on the shortest path principle ignores the strength and direction of the social relationship between nodes in the social network, and ignores the difference of influence between nodes, so that the search results cannot meet the needs of searchers. Considering the important role of the influence of nodes and the influence intensity between nodes in social network search, this paper proposes the path optimization principle of maximizing the comprehensive influence, and constructs a new search algorithm based on this strategy by applying the modified Dijkstra algorithm to solve the optimal path between nodes. Using the data of typical real social networks, it is verified that the path optimization algorithm based on the principle of maximizing comprehensive impact is better than the optimization algorithm based on the shortest path, and the search results are better interpretable to users. This paper had proposed a new influence maximization algorithm which has more advantages for solving social network search with high costs or benefits consideration by taking the influence intensity of nodes or between nodes into account. [ABSTRACT FROM AUTHOR]

This is the sixth special issue on Design and Manufacturing for Environmental Sustainability. As the first special issue on this topic was issued in 2009, the topic has thirteen years of history in this journal. Environmental sustainability was recognized as important for manufacturing even at that time. This is why the special issue was started in this journal. The seriousness of this topic has been increasing not only in Europe but also in Japan and other countries. One critical trend as of late has been the way that the attitude of companies regarding this issue, including their positions on carbon neutrality, circular economy, and biodiversity, has come to determine the value of the company in the eyes of, for example, environment, society, and governance (ESG) finance. Nowadays, we cannot say sustainability or business; we should pursue businesses that contribute to sustainability. This sixth special issue includes eighteen well-written papers, with one more paper to follow. This series of special issues has focused on topics related to ecodesign, recycling, remanufacturing, low-energy and low-emission manufacturing, and circular economy. The papers in this special issue are also deeply related to these topics. As a result of the thirteen-year history of the special issue, we have been able to learn some viewpoints specific to the series in this journal. This has led this special issue to consist of four subcategories, although they are not explicitly represented in the table of contents. The first subcategory consists of seven papers, from the first paper by K. Halada et al. to the seventh paper by A. Yoshimura et al. These papers are related to design, including evaluation, business, remanufacturing, and materials. Among others, the important viewpoint common to these papers is the "flow" of product lifecycles and materials. The second category starts with the eighth paper by C. Ye et al. and ends with the eleventh paper by Y. Yamashita et al. These papers deal with manufacturing issues in the context of the environmental sustainability. For example, the eighth paper deals with the crushing of a photovoltaic panel, and the nineth paper by T. Cheng et al. deals with detecting lithium-ion battery fires, which are a serious problem for recycling plants. The application of additive manufacturing to remanufacturing discussed in the tenth paper by N. Sato et al. is also an important topic in the field of sustainable manufacturing. The third subcategory consists of three papers, from the twelfth paper by J.-C. Tu et al. to the fourteenth by A. Y. T. Panuju et al. These are related to consumer behavior. The fourth subcategory, from the fifteenth paper by M. Kojima to the last paper by S. Tajima, deals with issues related to developing countries and locality. These two topics of consumer behavior and developing countries are indispensable to the discussion of environmental sustainability, yet they are rarely discussed in engineering journals. The editor believes that no other journal deals with these four subcategories in a special issue. Most of the papers, revised and extended in response to the editor's invitations, were originally presented at EcoDesign 2021: the 12th International Symposium on Environmentally Conscious Design and Inverse Manufacturing, which was held online. The editor sincerely thanks the authors and reviewers for their devoted work in making this special issue possible. We hope that these articles will encourage further research into design and manufacturing for environmental sustainability. [ABSTRACT FROM AUTHOR]

We here introduce three types of self-actuating and nonelectronic machines using chemical reactions and physicochemical transformations. Our strategy is to develop completely artificial and autonomous machines that do not rely on electronic components. We herein demonstrate Belousov-Zhabotinsky gel machines, active droplet machines, and paper machines. [ABSTRACT FROM AUTHOR]

The activation function introduces nonlinearity into convolutional neural network, which greatly promotes the development of computer vision tasks. This paper proposes elastic adaptively parametric compounded units to improve the performance of convolutional neural networks for image recognition. The activation function takes the structural advantages of two mainstream functions as the function's fundamental architecture. The SENet model is embedded in the proposed activation function to adaptively recalibrate the feature mapping weight in each channel, thereby enhancing the fitting capability of the activation function. In addition, the function has an elastic slope in the positive input region by simulating random noise to improve the generalization capability of neural networks. To prevent the generated noise from producing overly large variations during training, a special protection mechanism is adopted. In order to verify the effectiveness of the activation function, this paper uses CIFAR-10 and CIFAR-100 image datasets to conduct comparative experiments of the activation function under the exact same model. Experimental results show that the proposed activation function showed superior performance beyond other functions. [ABSTRACT FROM AUTHOR]

In this study, we introduce a system called Matrix Researcher2vec (MResearcher2vec) which generates researcher embedding vectors from their papers and research projects in researchmap and KAKENHI databases. The system includes data on 276,841 researchers, 6,161,592 papers, and research projects. Utilizing natural language processing techniques, the MResearcher2vec model extracts researcher vectors from the papers and research project summaries of KAKENHI grant recipients. The similarity between reseachers is then computed to visualize inter-researcher relationships. The machine learning results have been integrated into a web service, providing a novel approach for academic relationship mining. It can be applied in the matching of research contents and researchers in evaluation of industry-government-academia collaboration and joint research. It contributes in four aspects: (1) exchanges between researchers, (2) creation of opportunities for researchers and companies to connect, (3) further promotion of interdisciplinary research, and (4) reduction of lost opportunities for research institutions to acquire talents. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant development in unmanned platform technologies, specifically unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). As a result, their application scenarios have expanded considerably. Unmanned platforms are considered integral components of the Internet of Things system. However, certain challenges arise when dealing with specialized tasks, such as navigating complex urban low-altitude terrain with multiple obstacles and limited communication capabilities. These challenges can greatly impact the efficiency of the system due to information isolation. To address this issue, a messenger drone mechanism is introduced in this paper, which utilizes air superiority to facilitate indirect communication between unmanned platforms. Additionally, a task sequence planning algorithm based on sampling transformation is designed. This algorithm efficiently assigns the drone to mobile UGVs by discretely sampling their paths and considering the UAV-UGV motion relationship. By transforming the problem into an asymmetric traveler problem, it allows for a fast solution. Finally, the effectiveness of the algorithm is verified through comparative analysis in different scenarios. [ABSTRACT FROM AUTHOR]

This paper estimates the state transition of COVID-19 positive cases by analyzing the data about confirmed positive cases in Tokyo, Japan. The prediction of the number of newly infected persons is one of the active research topics for the COVID-19 pandemic. Although such a prediction is important for recognizing the future risk of spreading infectious diseases, understanding the state transition after they are confirmed to be positive is also important for estimating the number of required ICUs, hotel rooms for isolation, etc. This paper classifies the state after being positive into "in hotel/home for isolation," "in hospital with a mild state," "in hospital with a severe state," "recovered," and "dead" and estimates the transition probabilities among those states from the data about confirmed positive cases in Tokyo, Japan. This paper shows the parameters estimated from different periods and discusses the difference considering the pandemic situation. An agent simulation using the estimated transition probabilities as its parameters is also proposed. The result of the simulation from August to November 2020 shows the predicted number of agents is close to the actual data. As one of the possible applications to the proposed agent simulation, this paper shows the simulation result from December 2020 to January 2021 under a hypothetical situation. [ABSTRACT FROM AUTHOR]

Sequential decision-making under multiple objective functions includes the problem of exhaustively searching for a Pareto-optimal policy and the problem of selecting a policy from the resulting set of Pareto-optimal policies based on the decision maker's preferences. This paper focuses on the latter problem. In order to select a policy that reflects the decision maker's preferences, it is necessary to order these policies, which is problematic because the decision-maker's preferences are generally tacit knowledge. Furthermore, it is difficult to order them quantitatively. For this reason, conventional methods have mainly been used to elicit preferences through dialogue with decision-makers and through one-to-one comparisons. In contrast, this paper proposes a method based on inverse reinforcement learning to estimate the weight of each objective from the decision-making sequence. The estimated weights can be used to quantitatively evaluate the Pareto-optimal policies from the viewpoints of the decision-makers preferences. We applied the proposed method to the multi-objective reinforcement learning benchmark problem and verified its effectiveness as an elicitation method of weights for each objective function. [ABSTRACT FROM AUTHOR]

In the new, dry-process method of cement production, the temperature of cement rotary kiln sintering zone is a key factor in ensuring the quality of cement clinker. Based on the auto-regressive with extra inputs model, a finite control set model predictive control with soft constraint of the generalized triangular interval is proposed in this paper for the characteristics of a cement rotary kiln calcination system with multi-variable, multi-time delay, bounded disturbance, and slow dynamic process. Simulation experiments show that the steady-state error of the control algorithm proposed in this paper is smaller with better anti-disturbance performance than that of the traditional reference-trajectory-constrained, predictive control algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a heat demand prediction model that analyzes the heat behavior of heat users, and analyzes the heat behavior data of heat users through a clustering algorithm to predict their heat demand. This paper is based on the supply-demand balance strategy to reduce the heat loss during the transmission of heat medium, and then improve the energy-saving efficiency of the boiler. The traditional boiler energy-saving and consumption reduction method is to optimize the boiler combustion parameters, improve the fuel combustion efficiency and waste heat recovery technology through the Internet of Things and big data technology. The method of balancing the heat-using end with the load of the boiler at low usage frequency is seldom considered. Therefore, this paper predicts the heat demand of the heat-using end by analyzing its thermal behavior, and balances the heat demand and boiler heat supply under the condition of meeting the heat demand. Finally, through simulation experiments, the validity of the model is verified, and the trend and data can be well predicted in the short-term heat demand prediction. [ABSTRACT FROM AUTHOR]

With the increasing size of financial assets and the complexity of monetary patterns, countries around the world are gradually becoming more transparent in their monetary policies, using central bank monetary policy communication as a new type of monetary policy instrument. To measure central bank communication more accurately, this paper proposes a dynamic topic model, LDA-BP, based on branching processes, to construct the central bank communication index. At the same time, this paper does four things: it uses the constructed communication index as a proxy variable for the new monetary policy instrument; it builds a TVP-FAVAR model that can extract potential macroeconomic information from many variables, and its time-varying nature can better reflect the dynamic regulatory effect of monetary policy; it constructs a three-dimensional impulse response diagram; and it conducts a systematic analysis of macroeconomic impact. The experimental results of demonstrate its effectiveness on central bank monetary policy communication, as it captures timely information about conventional monetary policy instruments and immediately responds to changes in interest rates and money supply. All three monetary policy instruments are effective in smoothing output volatility, with monetary policy communication having a longer-term impact on the macroeconomy. [ABSTRACT FROM AUTHOR]

The national earthquake and national volcanic eruption prediction programs of Japan started in 1965 and 1974, respectively, based on the recommendation of the Geodesy Council. As a result, observations of seismic and volcanic activity have been enhanced and our understanding of the mechanisms of earthquakes and volcanic eruptions has made significant advances. However, these research results did not fully contribute to mitigating the damage caused by the 1995 Kobe earthquake or the 2011 Tohoku earthquake. In order to make further contributions to disaster mitigation, we recognize the importance of research to apply the scientific understanding of earthquakes and volcanic eruptions in collaboration with researchers in related research fields. The Earthquake and Volcano Hazards Observation and Research Program (2014–2018) was thus started on the basis of a recommendation of the Council for Science and Technology. In this Program, research in the area of forecasting the hazards of earthquakes and volcanic eruptions has become one of the main pillars of research. Disaster information and knowledge of the disaster process, which involve the interaction of hazards and natural and social vulnerability, have been investigated in cooperation with earth scientists, engineers, and social scientists. On the basis of our results, research on literacy for disaster resilience was selected as one of the main pillars of research in the Second Observation and Research Program of Earthquake and Volcano Hazards (2019–2023), because a better understanding of and immediate information about disasters are important for disaster mitigation. This special issue, which aims to disseminate the results of almost five years of research activities on literacy for disaster resilience in that Program, includes 15 papers and survey reports from multidisciplinary fields of study. The collection begins with Kimura and Ikeda's important paper discussing the conceptual structure of the knowledge and abilities necessary to build disaster resilience capacities by analyzing school and community-based efforts, followed by a variety of empirical studies that are loosely categorized into two groups. The first group of papers concerns the understanding of disaster processes and mechanisms themselves as a factor inducing disaster prevention and mitigation. First, Ebina and Sugawara, and Sugimori attempt to learn lessons from the pre-modern earthquake disasters based on past picture maps and scrolled records, respectively, followed by Ohkura's interesting paper that points to problems in the Aso Volcano disaster mitigation system by analyzing the 2021 phreatic eruption process. Next, utilizing information and communication technology to support effective evacuation, Inoguchi, and Shiozaki and Hashimoto develop a disaster management toolkit and the tsunami drill program, respectively. The following two papers of Takubo et al. discuss behavioral mechanisms in a disaster period based on their simulation experiments from a cognitive science perspective. The second group investigates methods of enhancing people's literacy for disaster resilience, paying special attention to the interaction between science and civil society, led by Sawada and Sato, and Muroi, both discussing the disaster risk reduction efforts of small and medium-sized enterprises and of community-based organizations, respectively, in anticipation of the Nankai Trough earthquake and tsunami. Next, Kimura and Aikawa propose an education program to enhance the disaster awareness of high school students who have never experienced disasters. Further, four papers deal with the issue of people's access to scientific knowledge focusing on the roles of university institutes, including case studies of so-called open science initiatives, by Yamori, Nakamichi, and Sakamoto and Nakamichi, and finally, a comparative study of several research centers in volcanic localities by Horii et al. These studies argue for the importance of the concept of literacy for disaster resilience to embed scientific knowledge in society and interconnect theory and practice, but the study of such a concept, especially in relation to earthquakes and volcanic eruptions, has just begun. We hope that this issue will encourage discussions from a wide range of scientists, experts in education and disaster management, and policy makers. [ABSTRACT FROM AUTHOR]

As online resources such as social media are increasingly used in disaster situations, confusion caused by the spread of false information, misinformation, and hoaxes has become an issue. Although a large amount of research has been conducted on how to suppress disinformation, i.e., the widespread dissemination of such false information, most of the research from a revenue perspective has been based on prisoner's dilemma experiments, and there has been no analysis of measures to deal with the actual occurrence of disinformation on disaster SNSs. In this paper, we focus on the fact that one of the characteristics of disaster SNS information is that it allows citizens to confirm the reality of a disaster. Hereafter, we refer to this as collective debunking, and we propose a profit-agent model for it and conduct an analysis using an evolutionary game. As a result, we experimentally found that deception in the confirmation of disaster information uploaded to SNS is likely to lead to the occurrence of disinformation. We also found that if this deception can be detected and punished, for example by patrols, it tends to suppress the occurrence of disinformation. [ABSTRACT FROM AUTHOR]

Most of the existing trackers based on discriminative correlation filters use only one feature or a simple linear fusion of multiple features for object tracking, and most of them lack a mechanism to handle occlusions. This leads to poor tracking performance in rapidly changing and easily occluded scenarios, especially on unmanned aerial vehicle (UAV) platforms. To address this issue, this paper proposes an anti-occlusion visual tracking algorithm for UAVs with multi-feature adaptive fusion named multi-feature adaptive fusion and anti-occlusion tracker (MAFAOT). It introduces a novel approach for implementing an adaptive fusion of multiple features. This method transforms the multi-feature fusion problem into a maximization issue by designing a tracking quality evaluation index. It successfully achieves an adaptive fusion of gradient direction histogram and color histogram feature responses. MAFAOT also introduces an anti-occlusion update pool strategy, enabling the tracker to adapt dynamically to various complex scenarios, including occlusion and motion blur. The experimental results on the OTB100 and UAV123 datasets confirm the significant advantages of MAFAOT in terms of precision and success rate compared to other correlation filter-based algorithms. The proposed methods further enhance the expressiveness of the features and effectively avoid the problem of tracker contamination caused by occlusion. Furthermore, this paper applies the proposed methods to the kernelized correlation filters (KCF) algorithm. On the OTB100 dataset, the improved KCF algorithm shows an improvement of 10.94% in precision and 11.11% in success rate. On the UAV123 dataset, it shows an improvement of 14.53% in precision and 16.62% in success rate, further verifying the effectiveness and versatility of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper proposes a simple knowledge graph embedding (KGE) framework that considers the entity type information without additional resources. The KGE is used to obtain vector representations of entities and relations by learning structured information in triples. The obtained vectors are used to predict the missing links in a knowledge graph (KG). Although many KGs contain entity type information, most of the existing methods ignored the potential of the entity type information for the link prediction task. The proposed framework, which is called entity and entity type composition representation learning (EETCRL), obtains vector representations of both entities and entity types, which are combined and used for link prediction. Experimental results on three datasets show that the EETCRL outperforms the baseline methods in most cases. Furthermore, the results obtained from tests with different model sizes show that the proposed framework can achieve high performance even with a small model size. This paper also discusses the effect of considering information about entity types on the link prediction task by analyzing the experimental results. [ABSTRACT FROM AUTHOR]

The United Kingdom is the third-largest peer-to-peer (P2P) lending market in the world, which is surpassed only by the two dominant forces in P2P investing, China and the United States of America. As an innovative financial market in the UK, P2P lending brings not only many opportunities but also many risks, especially the loan default risk. In this context, this paper uses binary logistic regression and survival analysis to evaluate default risk and loan performance in UK P2P lending. The empirical results indicate that credit group, loan purpose for capital needs, sector type, loan amount, interest rate, loan term, and the age of the company all have a significant impact on the probability of loan default. Among them, the interest rate, loan term, and loan purpose for capital needs are the three most important determinants of the probability of loan defaults and survival time of loans. [ABSTRACT FROM AUTHOR]

Blast furnace gas (BFG) is an important secondary energy in the iron and steel industries, and its efficient and reasonable utilization is the key to improving the economic efficiency of enterprises and the level of energy conservation and emission reduction. Aiming at the problems of difficult accurate modeling on the generation process and difficult prediction of real-time flow, this paper proposes a generation prediction model based on spectrum decomposition. Firstly, the major chemical reactions, production process, and data characteristics of blast furnace are analyzed, and the input variables for the prediction model are reasonably selected based on the correlation analysis results. Then, according to the spectrum characteristics, the BFG data is decomposed into low-frequency and medium-frequency parts by two finite impulse response filters. Next, for the low- and middle-frequency components of data, a low-frequency component prediction model based on the support vector regression, and a middle-frequency component prediction model based on the Elman neural network (ENN) are designed respectively. Finally, we decompose the spectrum of the actual industrial production data and find that the spectrum of the decomposed data basically meets the expected target, which verifies the effectiveness of the finite impulse response filters. In addition, we compare the prediction effect of the designed combined model with other models, such as the support vector regression, the back-propagation neural network, and the ENN. The final experimental results show the correctness, effectiveness, and superiority of the combined model and the spectral decomposition method proposed in this paper. [ABSTRACT FROM AUTHOR]

In biological and social systems, a "swarm" refers to a group of individual units that behave as a single intelligent entity. "Swarm behavior," the collective result of the local interactions among the group members, exhibits what is called "swarm intelligence." By identifying the design principles of such swarm intelligence, we may be able to create swarm robots that are highly adaptable, fault tolerant, and dimensionally flexible. An interdisciplinary approach, including disciplines ranging from technology to biology to the mathematical sciences, for example, is used to elucidate the design principles of swarm intelligence. We believe that such knowledge will lead to transformations in the field of swarm robotics. This special issue highlights 19 exciting papers, including 13 research papers, five review papers, and one letter. Some papers focus on understanding the mechanism of real swarm phenomena, while the other papers focus on designing intelligent swarm systems. The keywords of the papers are as follows. • Swarm intelligence • Interdisciplinary approach • Decentralized control • Swarm robot • Collective behavior We would like to express our gratitude to all authors and reviewers, and we hope that this special issue contributes to future research and development in swarm intelligence. [ABSTRACT FROM AUTHOR]

In recent years, the field of manufacturing has become increasingly diversified and complex to meet various requirements, such as improved manufacturing accuracy, productivity, environmental friendliness, and automation/intelligence in manufacturing sites. Consequently, the fusion of different fields as well as interdisciplinary research has become indispensable for the creation of innovative manufacturing technologies that can realize advanced production systems. This special issue comprises seven outstanding research papers that focus on advanced precision engineering in manufacturing systems, covering the following topics: - Advanced cutting technologies - Advanced machine tools and elements - Surface finishing technologies - Non-traditional machining and additive manufacturing - Nano-scale surface finishing - Advanced surface processing All research papers were originally presented at the 19th International Conference on Precision Engineering (ICPE2022), held in Nara, Japan, in 2022. The editors hope that the research papers included in this special issue will offer valuable insights to readers for their future research in the field of manufacturing technology. Each paper underwent a rigorous peer-review process, and the editors would like to express their deep appreciation for the efforts and excellent work of all the authors and anonymous reviewers who contributed to the realization of this special issue. Finally, it is our sincere hope that the papers in this special issue will further contribute to the advancement of our future society. [ABSTRACT FROM AUTHOR]

The Journal of Disaster Research has published a special issue on NIED Frontier Research on Science and Technology for Disaster Risk Reduction and Resilience 2023. The issue includes three papers and one tutorial. The first paper discusses the orientation changes caused by earthquake movements in a seafloor observation network for earthquakes and tsunamis along the Japan Trench. The second and third papers focus on research done on volcanoes, specifically the seismic activity of the Nasudake volcano and an experiment aimed at monitoring climbers and sharing the level of disaster threat in real time. The last paper is a tutorial on a standard for synthesizing and sharing common information from multiple organizations. The National Research Institute for Earth Science and Disaster Resilience (NIED) believes that the articles in this special issue can contribute to the advancement of science and technology for disaster risk reduction and resilience. [Extracted from the article]

To ensure the safe operation of the power system, it is necessary to monitor and manage all kinds of power facilities. Real-time power monitoring can play a role in the early warning of geographical disasters. At the transmission end, the state detection and fault location of transmission lines are related to the normal operation of the power system. The precise time synchronization of the power system can be guaranteed through the satellite positioning time service, so that the time of various power operation actions is consistent. The accuracy can reach several microseconds, which provides a basis for the analysis of power grid operation. In this paper, by installing BeiDou functional modules in power equipment and developing the BeiDou operation platform for the distribution network, the precise management of power timing and positioning is realized by docking with the geographic information systems platform of the power grid. The three-dimensional sphere error probability (SEP) algorithm is selected to evaluate the positioning error data. Based on the three traditional SEP calculation methods, the maximum likelihood estimation algorithm is optimized and improved, which can achieve nanosecond synchronization accuracy. It can also be applied to other precision of synchronization scenarios of the power system to meet the development needs of the future clock system of the power system. Through the simulation of experimental data and comparison with other algorithms, the accuracy is verified and the reliable estimation of parameters is realized. Considering the information loss caused by dimension reduction evaluation, the simulation analysis in this paper is compared with other methods, and the accuracy is significantly improved. The power system positioning and timing module developed in this paper can not only be used in the power system but also has a certain reference and application value for other devices in the same industry that need timing and positioning services. [ABSTRACT FROM AUTHOR]

This paper focuses on latecomers in technology standards alliances, a topic that has received limited attention in academia. Although latecomers have disadvantages in terms of technology and market access, they possess a strong desire to catch up technologically. As a fundamental characteristic of an alliance, alliance routines serve as a source of innovation for members and a basic unit of analysis, providing a new perspective for understanding and researching the technological catch-up of latecomers. In this paper, a questionnaire survey is conducted among 83 latecomer enterprises in a technology standards alliance. The survey covers strategic emerging industries, such as new energy, new-generation information technology, new materials, and high-end manufacturing. Then, hierarchical regression is performed for hypothesis testing. The research shows that the three dimensions of alliance routines significantly promote the utilization of technological catch-up by latecomer enterprises. However, the impact on the exploration technological catch-up varies, where the action logic promotes exploration technological catch-up while implicit norms hinder exploratory innovation with an inverted U-shaped impact. The absorptive capacity strengthens the positive impact of the three alliance routines on the exploitative innovation of latecomer enterprises and strengthens the inverted U-shaped relationship between mutual consensus and their exploratory innovation. Nonetheless, it fails to play a significant regulatory role in action logic or in the relationship between implicit norms and exploratory innovation of latecomers. [ABSTRACT FROM AUTHOR]

The development of the digital economy in the modern era significantly embodies a country's comprehensive strength. This paper uses the data from China's 2012 and 2017 input-output tables to analyze the linkage structure between the digital economy industry and other industries using the average propagation length model and social network analysis. The Weaver–Thomas model was used to obtain the industrial linkage matrix and network characteristics. An industrial chain map of the digital economy was depicted as the core. This paper is of practical significance in depicting the industrial chain structure and network characteristics, understanding the position of digital economy industry in the economic system and promoting the integrated development of the digital economy and traditional manufacturing industries, which allows them to incorporate the new generation of technology and industrial transformation with the digital technology as the core to promote their transformation and upgrading. [ABSTRACT FROM AUTHOR]

The 1923 Kanto earthquake (MJ=7.9) has great significance for seismic hazard assessments of the Tokyo metropolitan area, because it caused extensive damage to the area. To deepen our understanding of the strong ground motion of the Kanto earthquake, this paper reviewed studies on its observed strong ground motion and found that 1) seismic intensity VI on the Japanese scale (IX or X on the M. M. scale) was widely distributed in an area of approximately 8,000 km2, which is equivalent of that of the 2011 M9 Tohoku earthquake, 2) strong effects of soil amplification on ground motion were suggested by the correlation of the damage distribution with surface geology, 3) the restoration of the scaled-out seismogram indicated that the ground motion at the upland of Tokyo would be roughly 50 cm/s for maximum velocity and 50 cm for maximum displacement, and 4) the ground motion in the epicentral area is unclear, but would be roughly twice or more as strong as that at Tokyo. [ABSTRACT FROM AUTHOR]

In recent years, cooperative transportation systems using multiple mobile robots have been studied. The advantage will be to transport objects that cannot be dealt with by a single robot and transport them by using smaller robots. Although cooperative transportation by a group of robots has been studied, the conventional transportation targets are limited to objects whose posture is stable. In this paper, we propose a system in which robots pile up on each other to support an object, aiming at a system for more versatile object transportation, including unstable objects. After deriving the conditions by modeling the support system in object transportation, we verify the transporting performance including the robotic pile-up effect through actual robot experiments. [ABSTRACT FROM AUTHOR]

This paper presents a method of generating collective behavior of a multi-legged robotic swarm using deep reinforcement learning. Most studies in swarm robotics have used mobile robots driven by wheels. These robots can operate only on relatively flat surfaces. In this study, a multi-legged robotic swarm was employed to generate collective behavior not only on a flat field but also on rough terrain fields. However, designing a controller for a multi-legged robotic swarm becomes a challenging problem because it has a large number of actuators than wheeled-mobile robots. This paper applied deep reinforcement learning to designing a controller. The proximal policy optimization (PPO) algorithm was utilized to train the robot controller. The controller was trained through the task that required robots to walk and form a line. The results of computer simulations showed that the PPO led to the successful design of controllers for a multi-legged robotic swarm in flat and rough terrains. [ABSTRACT FROM AUTHOR]

In recent years, research has been conducted on swarm robot systems in which multiple autonomous mobile robots cooperate to perform tasks. Swarm robot systems are expected to perform high functionality as a group by cooperating with each other, in spite of the limited capabilities of the individual robots. This paper explores a method of simplifying swarm robot controllers as much as possible for swarm robot navigation. If we can achieve autonomous navigation of swarm robots to a target area with minimal resource consumption, they only need to implement the task execution function in that area. This leads to lower costs for swarm robot design and more efficient system architecture design. To address the above aims, this study focuses on heterogeneity. Specifically, we introduce a navigator robot that indirectly guides swarm robots named the worker robots. Heterogeneity in this paper refers to the worker robots and the navigator robots. We design the interaction between the navigator robot and the worker robots to provide a system that guides the worker robots to the destination. [ABSTRACT FROM AUTHOR]

To increase an accuracy of the sleep stage estimation without connecting any devices/electrodes to the body, this paper proposes the updating method for its estimation according to an ultradian rhythm as one of the biological rhythms of humans. In the proposed method, the prediction probability of the sleep stage is updated by the Widrow–Hoff learning rule which is generally employed in the update of reinforcement learning. Through the human subject experiment which acquired the biological vibration data from the mattress sensor during sleep, the following implications have been revealed: (1) the accuracy and the quadratic weighted kappa (QWK) of the sleep stage estimation updated by the proposed method are higher than those of random forest (RF) as the conventional method; (2) the multiple update of the probability of the sleep stage according to the ultradian rhythm is significantly important to improve its accuracy and QWK; and (3) compared with RF which over-estimated the NR2 stage while less-estimated the NR1 and NR3 stages, the proposed method contributes to correctly estimating the NR1–3 stages thank to the follow of the ultradian rhythm. [ABSTRACT FROM AUTHOR]

This paper proposes a machining method that uses ultrasonic vibration in the radial direction of the grinding wheel. This method is expected to suppress machining heat because the wheel and workpiece are in intermittent contact with each other. The abrasive grains on the working surface of the wheel act dynamically in the direction of cutting into the workpiece. In this paper, the constant-load grinding of Al2O3 ceramics, a hard and brittle material, was performed. Ductile-mode surfaces were more easily obtained when vibration support was used. The standard deviation of the brightness distribution of the ground surface can be used to evaluate the ductile-mode surface. In this study, the value was less than 16. The results of the measurement of tangential/normal grinding force ratio and the rear-surface temperature of the workpiece confirmed that the values were higher when ductile-mode machining was performed, compared with brittle-mode machining. Furthermore, a vibration-assisted removal model was used to discuss the results of the ductile-mode surfaces, which were more easily obtained when vibration-assisted machining was used. [ABSTRACT FROM AUTHOR]

Sakurajima Volcano Observatory, with a history exceeding 60 years, is recognized by researchers in Japan and overseas for conducting frontier research centering on Sakurajima, the most active volcano in Japan. It is owned by Kyoto University and represents the university in the surrounding communities. This paper reviews the open-door events, which have been conducted yearly for the last ten years as a part of a university-wide effort by Kyoto University. They make use of the facilities at Sakurajima Volcano Observatory, which aims to enhance the understanding of volcanoes among the general public in collaboration with geo-guides as "boundary workers." [ABSTRACT FROM AUTHOR]

"Multi-agent systems (MAS)" have been extensively studied across various fields, including robotics, economics, biology, and computer science. A distinctive feature of these systems is the ability of multiple agents, each with different characteristics, to perform system-wide tasks through local bottom-up interactions. Furthermore, design and control methods for system networks based on graph theory are being developed. Recent applications of these methods include autonomous driving technology, smart grids, and understanding social systems. This special issue aims to deepen the understanding of MAS, focusing on their control and applications. It features 16 papers, including one review paper. The accepted papers cover a wide range of topics, including reinforcement learning, autonomous mobility systems, and machine learning, presenting the latest research findings on MAS. These studies provide valuable insights into various aspects and potential applications of MAS. We hope that this issue will be beneficial to our readers and contribute to the advancement of future research. [ABSTRACT FROM AUTHOR]

This paper describes a method for generating toolpaths based on machining strategies for five-axis controlled machining using special tools. Traditionally, most toolpath generation studies focused on ball-end mills, proposing strategic methods to achieve high-quality machining while avoiding tool interference. Recently, special finishing tools with large cutting edge radii have gained interest for achieving higher machining efficiency. These special tools can produce smooth finished surfaces even with large pick-feed widths, leading to higher productivity. However, unlike conventional machining with ball-end mills, five-axis controlled machining using special tools lacks standardized work design procedures. This study proposes a generic tool-geometry data format for defining special tool geometries and a method for generating toolpaths using this data format. This method strategically treats special tools as conventional ball-end mills. Consequently, five-axis controlled machining for new tool geometries can be achieved using existing operational procedures. To generate toolpaths, this study utilizes a two-dimensional configuration space (C-Space). For special tools with multiple cutting edge radii, the relationship between the tool posture and cutting edge contact point is clarified by mapping the cutting edge radius information onto the C-Space. By employing this mapped cutting edge information, we can determine the interference-free tool posture corresponding to the chosen cutting edge section based on the machining strategy. Finally, the paper presents machining simulations and experiments conducted to confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper delves into the recent advancements of log-aesthetic curves (LAC), a family of curves for industrial design. Recently it has been redefined in the context of similarity geometry, a branch of Klein geometry. Building upon the work of Miura and Gobithaasan [1], the paper explores LAC properties, delves into the connection with similarity geometry (as detailed in Inoguchi et al. [2],[3],[4],[5]), and examines extensions of LACs, including the σ-curve [6] and τ-curve [7]. [ABSTRACT FROM AUTHOR]

In the aftermath of the devastating Palu disaster in September 2018, communities, especially children, suffered deep psychological trauma. At the request of faith leaders, local NGO Wahana Visi Indonesia (WVI), in partnership with Krida Wacana Christian University (UKRIDA), conducted a psychosocial support training program for volunteers, including Qur'an recitation teachers and Sunday school teachers in Palu. These trained volunteers then initiated a psychosocial support initiative for children in the area. Years later, WVI and UKRIDA conducted a reflective study of the program, collecting data from 40 volunteers through focus group discussions and surveys. The study identified key themes, including the effectiveness of the training, reflections on program implementation, lessons learned, and recommendations for future programs. The study underscores the importance of multi-stakeholder involvement, particularly that of faith-based organizations, in providing psychosocial support in disasteraffected areas. Given the recurring threat of disasters in the region, the study advocates for transparent collaboration among various social stakeholders. To build community resilience, disaster management strategies need to be widely disseminated, and the need for continuous training to strengthen community preparedness is emphasized. In addition, the development of culturally sensitive training modules tailored to the community's language, age, and religious background is essential. Such a comprehensive commitment will ensure the community's ability to deal effectively with future disasters. Detailed findings are discussed further in this paper. [ABSTRACT FROM AUTHOR]

This paper focuses on policy selection and scheduling of sensors and attackers in cyber-physical systems (CPSs) with multiple sensors under denial-of-service (DoS) attacks. DoS attacks have caused enormous disruption to the regular operation of CPSs, and it is necessary to assess this damage. The state estimation of the CPSs plays a vital role in providing real-time information about their operational status and ensuring accurate prediction and assessment of their security. For a multi-sensor CPS, this paper is different from utilizing robust control methods to characterize the state of the system against DoS attacks, but rather positively analyzes the optimal policy selection of the sensors and the attackers through dynamic programming ideology. To optimize the strategies of both sides, game theory is employed as a means to study the dynamic interaction that occurs between the sensors and the attackers. During the policy iterative optimization process, the sensors and attackers dynamically learn and adjust strategies by incorporating reinforcement learning. In order to explore more state information, the restriction on the set of states is relaxed, i.e., the transfer of states is not limited compulsorily. Meanwhile, the complexity of the proposed algorithm is decreased by introducing a penalty in the reward function. Finally, simulation results show that the proposed algorithm can effectively optimize policy selection and scheduling for CPSs with multiple sensors. [ABSTRACT FROM AUTHOR]

In the context of the new era, teachers and students in colleges and universities, as well as the general public, rely more on the Internet and social media to obtain news, express their opinions, and share information, and the dissemination of public opinion events in colleges and universities is not only related to the physical and mental health of teachers and students but also to the reform and development of colleges and universities. In this study, we took campus public opinion events as the main research object in which we selected three recent campus public opinion events to be analyzed. The public opinion data used in the research was collected from Weibo social media platforms. Firstly, we analyzed the dissemination cycle and regional dissemination patterns of a college food safety public opinion hot event through the popularity and regional distribution of public opinion data, thus revealing its formation and evolution patterns. Secondly, the LDA topic mining method is used to mine the themes of the three hot public opinion events, and then analyze the hot factors of the dissemination of each public opinion event from the massive public opinion data. This is crucial for the management department to grasp the dynamics of public opinion. Then, we used the SKEP sentiment classification method to analyze the emotional factors of the public opinion data of the three events to obtain the overall public opinion sentiment situation of the events. Finally, based on the characteristics of time, region, and gender, the evolution and diffusion rules of public topics and emotional distribution under different types of events are analyzed. The precision of the analyses associated with this paper may be limited to the effects of current mainstream as well as state-of-the-art analytical models. The analysis methods and conclusions in this paper provide a scientific theoretical basis and improvement measures for campus public opinion management, which helps to enhance the level of campus public opinion management and safeguard campus stability and public order. [ABSTRACT FROM AUTHOR]

Geometry processing is a software technology that facilitates the appropriate and efficient conversion and processing of digital geometric data, including sampled points, curves, surfaces, images, and volumetric data, based on the intended application. It is a versatile technology and is increasingly adopted in diverse fields that rely on digital technologies and for the popularization of digital transition. Geometry processing is based on a robust and extensive mathematical framework that has been extensively investigated. As the scope of potential applications expands, the practical applications of these theories have increased accordingly. Geometry processing is currently employed for shape acquisition, analysis, design, and inspection in various fields, such as manufacturing, social infrastructure, medicine, natural science, and art. As the range of applications expands, several issues have emerged that require further investigation, such as increasing the accuracy and managing large-scale data. The objective of this special issue is to provide new insights into geometric-processing capabilities. This issue comprises five papers that present novel aspects of this technology and two comprehensive reviews pertaining to aesthetic curves and related topics, as well as the applications of X-ray computed tomography (CT) in manufacturing. The papers included in this issue are as follows: - Aesthetic curves and related geometries - Object recognition from laser-scanned point cloud - CAD data compression via wavelet transform - Application of structure-from-motion - Image analysis using Delaunay graphs - Surface extraction from X-ray CT scan data - Automation of manufacturing using X-ray CT scanning The data types and methods used for geometry processing are diverse and depend on the scenarios. In this issue, we present a diverse range of studies pertaining to geometry processing that encompasses both fundamental concepts and applied studies. We are grateful to all the contributors, reviewers, and editorial staff for their dedication and support in realizing this issue. [ABSTRACT FROM AUTHOR]

This paper proposes a method for the point cloud data (PCD) map creation for the 3D LiDAR localization. The features of the method include the creation of a PCD map from a drawing of the buildings and partial scan of the not-existing object of the map by the tablet computer with the LiDAR. In the former, a map creation procedure, including the up- and down-sampling, as well as the processing, with voxel grid filter is established. In the latter, automatic position correction of the tablet scan data is introduced when they are placed to the current PCD map. Experiments are conducted to determine the size of the voxel grid filter and prove the effect of the tablet scan data in enhancing the matching level and the localization accuracy. Finally, the experiment with an autonomous mobile robot demonstrates that a map created using the proposed method is sufficient for autonomous driving without losing the localization. [ABSTRACT FROM AUTHOR]

This paper outlines a path planning method for autonomous rovers navigating urban environments without prior mapping, with a particular focus on addressing the Tsukuba Challenge. Our approach utilizes observations of pedestrian and robot movement trajectories to construct path graphs for global path planning. We provide a detailed overview of the autonomous rover's hardware and software system, as well as a comprehensive description of the path planning algorithm. Our methodology entails extracting and continuously tracking dynamic objects from LiDAR data, resulting in the creation of a path graph based on their observed trajectories. Subsequently, a path aligned with the desired direction is selected. Notably, in indoor experimental settings, our approach proves effective, as the rover successfully generates a path to the goal by closely monitoring and tracking pedestrian movements. In conclusion, this paper introduces a promising path planning methodology and suggests potential areas for further research in autonomous mobility within uncharted environments. [ABSTRACT FROM AUTHOR]

Visual place recognition from a 3D laser LiDAR is one of the most active research areas in robotics. Especially, learning and recognition of scene descriptors, such as scan context descriptors that map 3D point clouds to 2D point clouds, is one of the promising research directions. Although the scan-context descriptor has a sufficiently high recognition performance, it is still expensive image data and cannot be handled with low-capacity non-deep models. In this paper, we explore the task of compressing the scan context descriptor model while maintaining its recognition performance. To this end, the proposed approach slightly modifies the off-the-shelf classifier model of convolutional neural networks (CNN) from its basis, by replacing the SoftMax part with a support vector machine (SVM). Experiments with publicly available NCLT dataset validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose an inchworm-type soft robot using a self-healing gel as its body and shape-memory alloy (SMA) wires as its actuators. To realize inchworm-like locomotion, two coiled SMA wires are placed in parallel in the gel-fabricated body. The bottom-side wire and the upper-side wire reciprocally bend by applying electric current to the actuators. To realize the self-restoration automatically, the robot consists of a self-healing body equipped with magnets. The paper introduces the structure of the inchworm-shaped robot with its inchworm-like locomotion performance, together with the self-healing function. [ABSTRACT FROM AUTHOR]

This study proposes an object detection algorithm based on the improved YOLOv5 network for the uncivilized behavior of reclining public chair, which often occurs in cities. The current object detection field is studied by a single object. For the behavior of a lying public chair, the object to be measured is composed of two parts: the chair and the human posture jointly. Furthermore, the features of the object will show a large variability under different shooting angles, so the model's ability to extract features of the object is extremely important. This paper incorporates the Ghost module based on the YOLOv5 network to enable the model to learn more object features. The Ghost makes the neural network lighter by using linear convolution instead of nonlinear convolution, and its generated redundant features can help the model learn more object features and improve the model performance. In addition, this paper uses a new loss function EIoU to replace the original loss function CIoU. By comparison, EIoU solves the problem that CIoU fails in penalty terms under specific conditions. EIoU enables the model to converge faster and better. After experimental validation on the test set, it is shown that the improved YOLO network improves F1 by 3.5% and mAP by 4.2% compared to the original algorithm. [ABSTRACT FROM AUTHOR]