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Nuclear microreactors (MRs) have attracted significant attention for their versatility in installation locations, especially with the growing demand for sustainable green energy in large data processing facilities. However, ensuring their safe operation necessitates frequent inspections, which can be challenging for practical efficient management. This study proposes a novel approach using direct energy deposition to incorporate an optical fiber sensor into MR components, enabling real-time monitoring with artificial intelligence. The embedded optical fiber generates real-time data that allows for AI-driven in-vivo thermal deformation analysis. Our smart MR component can detect structural anomalies, identify abnormal operations, and assess operational conditions through augmented reality interfaces and AI technology.

Evacuation time estimation (ETE) is crucial for the effective implementation of resident protection measures as well as planning, owing to its applicability to nuclear emergencies. However, as confirmed in the Fukushima case, the ETE performed by nuclear operators does not reflect behavioral features, exposing thus, gaps that are likely to appear in real-world situations. Existing research methods including surveys and interviews have limitations in extracting highly feasible behavioral features. To overcome these limitations, we propose a VR-based immersive experiment system. The VR system realistically simulates nuclear emergencies by structuring existing disasters and human decision processes in response to the disasters. Evacuation behavioral features were quantitatively extracted through the proposed experiment system, and this system was systematically verified by statistical analysis and a comparative study of experimental results based on previous research. In addition, as part of future work, an application method that can simulate multi-level evacuation dynamics was proposed. The proposed experiment system is significant in presenting an innovative methodology for quantitatively extracting human behavioral features that have not been comprehensively studied in evacuation. It is expected that more realistic evacuation behavioral features can be collected through additional experiments and studies of various evacuation factors in the future.

This paper proposes a novel method to impart intelligence to metal parts using additive manufacturing. A sensor-embedded metal bracket is prototyped via a metal powder bed fusion process to recognise partial screw loosening or total screw missing or identify the source of vibration with the assistance of artificial intelligence (AI). The digital metal bracket can recognise subtle changes in the screw fixation state with 90% accuracy and identify unknown sources of vibration with 84% accuracy. The von Mises stress distribution in the prototyped metal bracket is evaluated using a finite element analysis, which is learned by AI to match the real-time deformation analysis of the metal bracket in augmented reality. The proposed prototype can contribute to hyper-connectivity for developing next-generation metal-based mechanical components.

During the last few decades, insulated-gate bipolar transistor (IGBT) power modules have evolved as reliable and useful electronic parts due to the increasing relevance of power inverters in power infrastructure, reliability enhancement, and long-life operation. Excessive temperature stresses caused by excessive power losses frequently cause high-power-density IGBT modules to fail. As a result, module temperature monitoring techniques are critical in designing and selecting IGBT modules for high-power-density applications to guarantee that temperature stresses in the various module components remain within the rated values. In this paper, a module’s different losses were estimated, a heating pipe system for the thermal power cycling technique was proposed, and finite element method (FEM) thermal modeling and module temperature measurement were performed using ANSYS Icepak software version 2022 R1 to determine whether the IGBT module’s temperature rise was within acceptable bounds. To test the proposed technique, a proposed design structure of the practical railway application with a 3.3 MW traction inverter is introduced using commercialized IGBT modules from Semikron company with maximum temperature of about 150 °C. the FEM analysis results showed that the maximum junction temperature is about 109 °C which is in acceptable ranges, confirming the appropriate selection of the employed IGBT module for the target application.

The core challenge in directed energy deposition is to obtain high surface quality through process optimisation, which directly affects the mechanical properties of fabricated parts. However, for expensive materials like Ti-6Al-4V, the cost and time required to optimise process parameters can be excessive in inducing good surface quality. To mitigate these challenges, we propose a novel method with artificial intelligence to generate virtual surface morphology of Ti-6Al-4V parts by given process parameters. A high-resolution surface morphology image generation system has been developed by optimising conditional generative adversarial networks. The developed virtual surface matches experimental cases well with an Fréchet inception distance score of 174, in the range of accurate matching. Microstructural analysis with parts fabricated with artificial intelligence guidance exhibited less textured microstructural behaviour on the surface which reduces the anisotropy in the columnar structure. This artificial intelligence guidance of virtual surface morphology can help to obtain high-quality parts cost-effectively.

Abstract Using energy‐saving electrochromic (EC) displays in smart devices for augmented reality makes cost‐effective, easily producible, and efficiently operable devices for specific applications possible. Prussian blue (PB) is a metal‐organic coordinated compound with unique EC properties that limit EC display applications due to the difficulty in PB micro‐patterning. This work presents a novel micro‐printing strategy for PB patterns using localized crystallization of FeFe(CN)6 on a substrate confined by the acidic‐ferric‐ferricyanide ink meniscus, followed by thermal reduction at 120 °C, thereby forming PB. Uniform PB patterns can be obtained by manipulating printing parameters, such as the concentration of FeCl3·K3Fe(CN)6, printing speed, and pipette inner diameter. Using a 0.1 M KCl (pH 4) electrolyte, the printed PB pattern is consistently and reversibly converted to Prussian white (CV potential range: −0.2–0.5 V) with 200 CV cycles. The PB‐based EC display with a navigation function integrated into a smart contact lens is able to display directions to a destination to a user by receiving GPS coordinates in real time. This facile method for forming PB micro‐patterns could be used for advanced EC displays and various functional devices.

In this study, three types of synthetic fiber materials were evaluated, namely, DM20, SK78, and T147, to replace steel chains in shipbuilding and offshore fields with fiber chains as there is increasing demand for chains with lighter weights and improved usabilities. The strength and quasi-static stiffness were analyzed to select suitable yarns for the fiber chains. The durability of the yarn was evaluated by performing a 3-T (time to rupture) test as a specific tension level. The results of the experiment revealed excellent dynamic stiffness in DM20 and highest values of the windward and leeward stiffness in T147. 3-T linear design characteristic curves for a specific tension level were derived for the three types of fiber materials. The findings of this study can provide insights for improving strength and durability in fiber chain design.

Abstract Additive manufacturing (AM) which can be a suitable technology to personalize wearables is ideal for adjusting the range of part performance such as mechanical properties if high performance is not required. However, the AM process parameter can impact overall durability and reliability of the part. In this instance, user behavior can play an essential role in performance of wearables through the settings of AM process parameter. This review discusses parameters of AM processes influenced by user behavior with respect to performance required to fabricate AM wearables. Many studies on AM are performed regardless of the process parameters or are limited to certain parameters. Therefore, it is necessary to examine how the main parameters considered in the AM process affect performance of wearables. The overall aims of this review are to achieve a greater understanding of each AM process parameter affecting performance of AM wearables and to provide requisites for the desired performance including the practice of sustainable user behavior in AM fabrication. It is discussed that AM wearables with various performance are fabricated when the user sets the parameters. In particular, we emphasize that it is necessary to develop a qualified procedure and to build a database of each AM machine about part performance to minimize the effect of user behavior.

A simulation-based approach is proposed to study the protective actions taken by residents during nuclear emergencies using cognitive findings. Human perception-based behaviors are not heavily incorporated in the evacuation study for nuclear emergencies despite their known importance. This study proposes a generic framework of perception-based behavior simulation, in accordance with the ecological concept of affordance theory and a formal representation of affordance-based finite state automata. Based on the generic framework, a simulation model is developed to allow an evacuee to perceive available actions and execute one of them according to Newton’s laws of motion. The case of a shadow evacuation under nuclear emergency is utilized to demonstrate the applicability of the proposed framework. The illustrated planning algorithm enables residents to compute not only prior knowledge of the environmental map, but also the perception of dynamic surroundings, using widely observed heuristics. The simulation results show that the temporal and spatial dynamics of the evacuation behaviors can be analyzed based on individual perception of circumstances, while utilizing the findings in cognitive science under unavoidable data restriction of nuclear emergencies. The perception-based analysis of the proposed framework is expected to enhance nuclear safety technology by complementing macroscopic analyses for advanced protective measures.

Approaches for predicting financial markets, including conventional statistical methods and recent deep learning methods, have been investigated in many studies. However, financial time series data (e.g., daily stock market index) contain noises that prevent stable predictive model learning. Using these noised data in predictions results in performance deterioration and time lag. This study proposes padding-based Fourier transform denoising (P-FTD) that eliminates the noise waveform in the frequency domain of financial time series data and solves the problem of data divergence at both ends when restoring to the original time series. Experiments were conducted to predict the closing prices of S&P500, SSE, and KOSPI by applying data, from which noise was removed by P-FTD, to different deep learning models based on time series. Results show that the combination of the deep learning models and the proposed denoising technique not only outperforms the basic models in terms of predictive performance but also mitigates the time lag problem.

Power system disruptions can be categorized as issues with the quality of electricity brought on by voltage sags, lightning strikes, and other system-related interferences. The static transfer switch (STS) has recently emerged as the most important technology for electric power transmission, distribution, and control systems to manage power supply during power system disruption issues, particularly in cost-effectively supplying power to critical loads and sensitive loads without interruption. In this paper, for the switching between the two AC sources during the voltage disruptions issue with low transfer time, a smart static transfer switch (SSTS) based on a digital switching algorithm and Triac semiconductor switch is proposed and experimentally tested. A digital switching algorithm based on online AC voltage sensing and zero-crossing detection is proposed and implemented inside a DSP MCU. The printed circuit board (PCB) of the proposed SSTS is designed and manufactured for the experimental performance investigation with different AC input voltage conditions. A comparative study based on the advantages and disadvantages of the proposed SSTS system with the previous works is also presented. A smart static transfer switch with a transition time of less than one cycle and a digital protection technique during fault conditions is obtained in this work.

Increasing production variability while maintaining operation efficiency remains a critical issue in many manufacturing industries. While the adoption of mixed-model assembly lines enables the production of high product variety, it also makes the system more complex as variety increases. This paper proposes an information entropy-based methodology that quantifies and then minimizes the complexity through product sequencing. The theory feasibility is demonstrated in a series of simulations to showcase the impact of sequencing in controlling the system predictability and complexity. Hence, the framework not only serves as a tool to quantitatively assess the impact of complexity on total system performance but also provides means and insights into how complexity can be mitigated without affecting the overall manufacturing workload.

This paper proposes a method to improve the output torque of a six-phase permanent-magnet synchronous machine (PMSM) within the same current peak limit through a fifth-harmonic injection into each phase current of the stator. Compared to the fifth + seventh-harmonic current-injection method used to improve the output torque of the six-phase PMSM, the control system can be stably controlled, and the controller design complexity decreased. This is because the harmonic component was converted into a direct current (DC) component and controlled by a proportional-integral (PI) controller instead of the fifth + seventh-harmonic injection method, which converts the harmonic component into an alternating current (AC) component and controls it with a resonance controller. The appropriate fifth-harmonic ratio for maximum output torque through fifth-harmonic injection was selected through optimization using values analyzed via fast Fourier transform (FFT) for stator phase harmonic current terms caused by inverter nonlinearity and motor design errors. Therefore, it was possible to optimize the fifth-harmonic ratio to be injected without requiring torque modeling using the physical properties of the motor. The experimental results were obtained under the rated current condition with six-phase PMSM in the laboratory, and the average output torque increase under fifth-harmonic injection was about 5% compared to the method without harmonic injection.

This paper presents an agent-based modeling framework for affordance-based driving behaviors during the exit maneuver of driver agents in human-integrated transportation problems. We start our discussion from one novel modeling framework based on the concept of affordance called the affordancebased finite state automata (AFSA) model, which incorporates the human perception of resource availability and action capability. Then, the agent-based simulation illustrates the validity of the AFSA framework for the highway-lane-driver system. Next, the comparative study between real driving data and agent-based simulation outputs is provided using the transition diagram. Finally, we perform a statistical analysis and a correlation study to analyze affordance-based driving behavior of driver agents. The simulation results show that the AFSA model well represents the perception-based human actions and drivers' characteristics, which are essential for the design viewpoint of control framework of human driver modeling. This paper is also expected to benefit a designed control for autonomous/self-driving car in the future.

Despite the recent advances in manufacturing automation, the role of human involvement in manufacturing systems is still regarded as a key factor in maintaining higher adaptability and flexibility. In general, however, modeling of human operators in manufacturing system design still considers human as a physical resource represented in statistical terms. In this paper, we propose a human in the loop (HIL) approach to investigate the operator's choice complexity in a mixed model assembly line. The HIL simulation allows humans to become a core component of the simulation, therefore influencing the outcome in a way that is often impossible to reproduce via traditional simulation methods. At the initial stage, we identify the significant features affecting the choice complexity. The selected features are in turn used to build a regression model, in which human reaction time with regard to different degree of choice complexity serves as a response variable used to train and test the model. The proposed method, along with an illustrative case study, not only serves as a tool to quantitatively assess and predict the impact of choice complexity on operator's effectiveness, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput.

Efforts to enhance thermal efficiency of turbines by increasing the turbine inlet temperature have been further accelerated by the introduction of 3D printing to turbine components as complex cooling geometry can be implemented using this technique. However, as opposed to the properties of materials fabricated by conventional methods, the properties of materials manufactured by 3D printing are not isotropic. In this study, we analyzed the anisotropic thermal conductivity of nickel-based superalloy CM247LC manufactured by selective laser melting (SLM). We found that as the density decreases, so does the thermal conductivity. In addition, the anisotropy in thermal conductivity is more pronounced at lower densities. It was confirmed that the samples manufactured with low energy density have the same electron thermal conductivity with respect to the orientation, but the lattice thermal conductivity was about 16.5% higher in the in-plane direction than in the cross-plane direction. This difference in anisotropic lattice thermal conductivity is proportional to the difference in square root of elastic modulus. We found that ellipsoidal pores contributed to a direction-dependent elastic modulus, resulting in anisotropy in thermal conductivity. The results of this study should be beneficial not only for designing next-generation gas turbines, but also for any system produced by 3D printing.

In unpredictable disaster scenarios, it is important to recognize the situation promptly and take appropriate response actions. This study proposes a cloud computing-based data collection, processing, and analysis process that employs a crowd-sensing application. Clustering algorithms are used to define the major damage types, and hotspot analysis is applied to effectively filter critical data from crowdsourced data. To verify the utility of the proposed process, it is applied to Icheon-si and Anseong-si, both in Gyeonggi-do, which were affected by heavy rainfall in 2020. The results show that the types of incident at the damaged site were effectively detected, and images reflecting the damage situation could be classified using the application of the geospatial analysis technique. For 5 August 2020, which was close to the date of the event, the images were classified with a precision of 100% at a threshold of 0.4. For 24–25 August 2020, the image classification precision exceeded 95% at a threshold of 0.5, except for the mudslide mudflow in the Yul area. The location distribution of the classified images showed a distribution similar to that of damaged regions in unmanned aerial vehicle images.

Over the past decades, additive manufacturing has rapidly advanced due to its advantages in enabling diverse material usage and complex design production. Nevertheless, the technology has limitations in terms of quality, as printed products are sometimes different from their desired designs or are inconsistent due to defects. Warping deformation, a defect involving layer shrinkage induced by the thermal residual stress generated during manufacturing processes, is a major factor in lowering the quality and raising the cost of printed products. This study utilized a variety of thermal time series data and the K-nearest neighbors (KNN) algorithm with dynamic time warping (DTW) to detect and predict the warping deformation in the printed parts using fused deposition modeling (FDM) printers. Multivariate thermal time series data extracted from thermocouples were trained using DTW-based KNN to classify warping deformation. The results showed that the proposed approach can predict warping deformation with an accuracy of over 80% by only using thermal time series data corresponding to 20% of the whole printing process. Additionally, the classification accuracy exhibited the promising potential of the proposed approach in warping prediction and in actual manufacturing processes, so the additional time and cost resulting from defective processes can be reduced.

Three-dimensional (3D) printing is a new paradigm in customized manufacturing and allows the fabrication of complex structures that are difficult to realize with other conventional methods. Four-dimensional (4D) printing adds active, responsive functions to 3D-printed components, which can respond to various environmental stimuli. This review introduces recent ideas in 3D and 4D printing of mechanical multistable structures. Three-dimensional printing of multistable structures can enable highly reconfigurable components, which can bring many new breakthroughs to 3D printing. By adopting smart materials in multistable structures, more advanced functionalities and enhanced controllability can also be obtained in 4D printing. This could be useful for various smart and programmable actuators. In this review, we first introduce three representative approaches for 3D printing of multistable structures: strained layers, compliant mechanisms, and mechanical metamaterials. Then, we discuss 4D printing of multistable structures that can help overcome the limitation of conventional 4D printing research. Lastly, we conclude with future prospects.

This paper presents a complete mathematical design of the main components of 2 kW, 54 direct current (DC)–DC converter stage, which can be used as the second stage of the two stages of alternating current (AC)–DC telecom power supply. In this paper, a simple inrush current controlling circuit to eliminate the high inrush current, which is generated due to high input capacitor at the input side of the DC–DC converter, is proposed, designed, and briefly discussed. The proposed circuit is very easy to implement in the lab using a single metal–oxide–semiconductor field-effect transistor (MOSFET) switch and some small passive elements. PSIM simulation has been used to test the power supply performance using the value of the designed components. Furthermore, the experimental setup of the designed power supply with inrush current control is built in the lab to show the practical performance of the designed power supply and to test the reliability of the proposed inrush current mitigation circuit to eliminate the high inrush current at initial power application to the power supply circuit. DC–DC power supply with phase shift zero voltage switching (ZVS) technique is chosen and designed due to its availability to achieve ZVS over the full load range at the primary side of the power supply, which reduces switching losses and offers high conversion efficiency. High power density DC–DC converter stage with smooth current startup operation, full load efficiency over 95%, and better voltage regulation is achieved in this work.

Additive manufacturing (AM) became widespread through several organizations due to its benefits in providing design freedom, inventory improvement, cost reduction, and supply chain design. Process planning in AM involving various AM technologies is also complicated and scarce. Thus, this study proposed a decision-support tool that integrates production and distribution planning in AM involving material extrusion (ME), stereolithography (SLA), and selective laser sintering (SLS). A multi-objective optimization approach was used to schedule component batches to a network of AM printers. Next, the analytic hierarchy process (AHP) technique was used to analyze trade-offs among conflicting criteria. The developed model was then demonstrated in a decision-support system environment to enhance practitioners’ applications. Then, the developed model was verified through a case study using automotive and healthcare parts. Finally, an experimental design was conducted to evaluate the complexity of the model and computation time by varying the number of parts, printer types, and distribution locations.

The constantly increasing number of cars in the megacities is causing severe parking problems. To resolve this problem, many cities adopt parking guidance system as a part of intelligent transportation system (ITS). However, the current parking guidance system stays in its infant stage since the obtainable information is limited. To enhance parking management in the megacity and to provide better parking guidance to drivers, this study introduces an intelligent parking guidance system and proposes a new methodology to operate it. The introduced system considers both public parking and private parking so that it is designed to maximize the use of spatial resources of the city. The proposed methodology is based on the dynamic information related parking in the city and suggests the best parking space to each driver. To do this, two kinds of utility functions which assess parking spaces are developed. Using the proposed methodology, different types of parking management policies are tested through the simulation. According to the experimental test, it is shown that the centrally managed parking guidance can give better results than individually preferred parking guidance. The simulation test proves that both a driver’s benefits and parking management of a city from various points of view can be improved by using the proposed methodology.

In the title complex, [Cu(C13H8NOS)2], the CuII atom is coordinated by two N atoms and two O atoms from two bidentate benzothiazolphenolate ligands, forming a distorted tetrahedral geometry [dihedral angle between two N—Cu—O planes: 45.1 (2)°]. The dihedral angles between the benzothiazole ring systems and the phenol rings are 4.1 (4) and 5.8 (4)°, indicating an almost planar geometry. Weak intra- and intermolecular C—H...O hydrogen bonds are observed. In the crystal, weak π–π interactions between aromatic and thiazole rings [centroid–centroid distances = 3.626 (3) and 3.873 (3) Å] link the molecules into a two-dimensional supramolecular network along the bc plane.

In the complex cation of the title salt, [CrCl(C2H6OS)5]Cl2, the CrIII ion is coordinated by one chloride ligand and five O atoms from dimethyl sulfoxide (DMSO) ligands, leading to a slightly distorted octahedral coordination environment [O—Cr—O angles range from 86.69 (16) to 92.87 (16)°]. In the crystal, complex cations are arranged in hexagonally packed rows parallel to [010], with the chloride counter-anions situated in between. The interactions between cations and anions are mainly ionic in nature.

The title compound, C2H3N3OS, is a monoclinic (P21/c) polymorph of the previously reported triclinic structure [Kang et al. (2012). Acta Cryst. E68, o1198]. The asymmetric unit contains two independent molecules which are essentially planar, with r.m.s. deviations of 0.001 and 0.032 Å from the mean plane defined by the seven non-H atoms. In the crystal, N—H...N and N—H...O hydrogen bonds link the molecules into a sheet parallel to (111).

The title compound, C9H9N3O2S, is a monoclinic (C2/c) polymorph of the previously reported triclinic structure [Kang (2013). Acta Cryst. E69, o1327]. The molecule is almost planar with an r.m.s. deviation of 0.069 Å from the mean plane of all non-H atoms. The benzoyl and terminal thiourea fragments adopt a transoid conformation with respect to the central carbonyl O atom. Two intramolecular N—H...O hydrogen bonds are present. In the crystal, N—H...O and N—H...S interactions link the molecules into zigzag chains extending along the c-axis direction.

In spite of it being necessary for humans to consider modeling, analysis, and control of human-included systems, it has been considered a challenging problem because of the critical role of humans in complex systems and of humans' capability of executing unanticipated actions–both beneficial and detrimental ones. Thus, to provide systematic approaches to modeling human actions as a part of system behaviors, a formal modeling framework for human-involved systems in which humans play a controlling role based on their perceptual information is presented. The theory of affordance provides definitions of human actions and their associated properties; Finite State Automata (FSA) based modeling is capable of mapping nondeterministic humans into computable components in the system representation. In this talk, we investigate the role of perception in human actions in the system operation and examine the representation of perceptual elements in affordance-based modeling formalism. The proposed framework is expected to capture the natural ways in which humans participate in the system as part of its operation. A human-machine cooperative manufacturing system control example and a human agent simulation example will be introduced for the illustrative purposes at the end of the presentation.