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Abstract This study optimizes VT and ΔVT in amorphous indium-gallium-zinc-oxide (a-IGZO) field-effect transistors (FETs) by examining the influence of both channel length (L) and Ga composition. It was observed that as the ratio of In: Ga: Zn changed from 1:1:1 to 0.307:0.39:0.303 in the IGZO film, both VT and ΔVT decreased by 0.1 V at the shortest channel length (L = 0.5 μm). This reduction was attributed to the change in the oxygen concentration in IGZO due to the variation in Ga composition. In addition, as the channel length decreased from 10 μm to 0.5 μm, VT decreased by up to 0.7 V, and ΔVT decreased by up to 0.4 V. This observation was due to diffusion of oxygen vacancies (VO) from the source and drain into the main channel. To provide a comprehensive understanding, we quantitatively modeled the doping concentration of IGZO and the trap density of gate insulator (GI) traps using TCAD simulation based on Ga composition and diffusion of VO. Using this approach, we propose a method to optimize the design of a-IGZO FETs with high VT and low ΔVT in short-channel devices by adjusting the Ga composition.

The objective of this study is to comprehensively evaluate the environmental and economic impacts of remanufactured constant velocity (CV) joints using a newly proposed framework for automotive parts replacement. This framework utilizes actual vehicle sales data to accurately estimate the demand for CV joint replacements. Specific parameters are established to calculate this demand, enabling a quantitative assessment of both environmental and economic impacts associated with remanufacturing CV joints and other automotive parts. Additionally, sensitivity analyses are conducted to explore the relationship between environmental benefits and economic outcomes, particularly focusing on the preferences for remanufactured parts and their profitability. The results indicate that increasing the proportion of remanufactured CV joints significantly benefits the environment by reducing CO2 emissions, raw material usage, and energy consumption. However, this shift also leads to a decrease in total profits within the CV joint replacement market. To address this trade-off, the study suggests that financial incentives, such as subsidies or tax benefits, are necessary to support the remanufacturing industry and facilitate market expansion. These findings provide valuable insights for policymakers aiming to promote sustainable manufacturing practices through effective subsidy policies.

Deep learning (DL) models require enormous amounts of data to produce reliable diagnosis results. The superiority of DL models over traditional machine learning (ML) methods in terms of feature extraction, feature dimension reduction, and diagnosis performance has been shown in various studies of fault diagnosis systems. However, data acquisition can sometimes be compromised by sensor issues, resulting in limited data samples. In this study, we propose a novel DL model based on a stacked convolutional autoencoder (SCAE) to address the challenge of limited data. The innovation of the SCAE model lies in its ability to enhance gradient information flow and extract richer hierarchical features, leading to superior diagnostic performance even with limited and noisy data samples. This article describes the development of a fault diagnosis method for a hydraulic piston pump using time–frequency visual pattern recognition. The proposed SCAE model has been evaluated on limited data samples of a hydraulic piston pump. The findings of the experiment demonstrate that the suggested approach can achieve excellent diagnostic performance with over 99.5% accuracy. Additionally, the SCAE model has outperformed traditional DL models such as deep neural networks (DNN), standard stacked sparse autoencoders (SSAE), and convolutional neural networks (CNN) in terms of diagnosis performance. Furthermore, the proposed model demonstrates robust performance under noisy data conditions, further highlighting its effectiveness and reliability.

A hydraulic piston pump is an essential component of a hydraulic transmission system and is extensively used in contemporary industrial settings. Therefore, fault diagnosis of piston pumps is a crucial topic in the engineering field. The convolutional neural network (CNN) is currently the most popular deep neural network model and has been successfully employed for fault detection and other tasks. The design and hyperparameter settings of CNNs significantly affect the overall diagnosis performance. In this study, a genetic method is proposed that can quickly investigate a specific set of potentially viable one-dimensional CNN (1D-CNN) architectures while also optimizing their hyperparameters for a fault detection task of an axial hydraulic piston pump. The proposed model is automatically designed based on a direct connect 1D-CNN block, which is another contribution of this study. The proposed method is evaluated on the raw sound signal dataset of an axial hydraulic piston pump without any signal pre-processing techniques. The experimental results demonstrate that the proposed method outperforms several well-known deep learning (DL) models in terms of fault diagnosis performance. Additionally, the suggested method uses significantly less computational power to determine the best 1D-CNN structures than most peer rivals.

Abstract Analog in‐memory computing synaptic devices are widely studied for efficient implementation of deep learning. However, synaptic devices based on resistive memory have difficulties implementing on‐chip training due to the lack of means to control the amount of resistance change and large device variations. To overcome these shortcomings, silicon complementary metal‐oxide semiconductor (Si‐CMOS) and capacitor‐based charge storage synapses are proposed, but it is difficult to obtain sufficient retention time due to Si‐CMOS leakage currents, resulting in a deterioration of training accuracy. Here, a novel 6T1C synaptic device using only n‐type indium gaIlium zinc oxide thin film transistor (IGZO TFT) with low leakage current and a capacitor is proposed, allowing not only linear and symmetric weight update but also sufficient retention time and parallel on‐chip training operations. In addition, an efficient and realistic training algorithm to compensate for any remaining device non‐idealities such as drifting references and long‐term retention loss is proposed, demonstrating the importance of device‐algorithm co‐optimization.

Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parcels regardless of their shape, pattern, or layout. The core of the algorithm is the utilisation of RGB-D technology for detecting the primary boundary lines regardless of obstacles such as adhesive labels, tapes, or parcel surface patterns. For cases where detecting the boundary lines is difficult owing to narrow gaps between parcels, we propose using deep learning-based boundary line detection through the You Only Look at Coefficients (YOLACT) model. Using image segmentation techniques, the algorithm efficiently predicts boundary lines, enabling the accurate detection of irregularly sized parcels with complex surface patterns. Furthermore, even for rotated parcels, we can extract their edges through complex mathematical operations using the depth values of the specified position, enabling the detection of the wider surfaces of the rotated parcels. Finally, we validate the accuracy and real-time performance of our proposed method through various case studies, achieving mAP (50) values of 93.8% and 90.8% for randomly sized and rotationally covered boxes with diverse colours and patterns, respectively.

This study investigated the tenderizing and flavor-enhancing effects of koji, a fermented grain cultured with a single microorganism, on chicken breasts during curing. Chicken breasts were cured with different ingredients, including 4% (w/w) curing agent (GC), 5% (w/w) Aspergillus oryzae with rice (FR), A. oryzae with soybean (FS), and Bacillus subtilis with soybean (BS) for 4 h at 4 °C prior to cooking. After the superheated steam procedure, all samples were cooked in a convection oven, and their physicochemical properties were analyzed. Koji-treated samples exhibited significantly higher expressible moisture due to the degradation of the protein matrix (p < 0.05). Texture profile analysis showed that the tenderness of koji-treated samples was significantly higher than that of GC (p < 0.05). Furthermore, koji-treated samples were regarded as tenderer, and they were preferred over GC (p < 0.05) in the sensory evaluation. Principal attributes analysis revealed that the overall preference for koji-treated samples was highly correlated with umami, juiciness, and tenderness (p < 0.05). Overall, this study provides insights into applying koji as a potential curing treatment to improve the eating quality of chicken breasts. Koji can be used as a novel technology in the food industry to improve taste and tenderness simultaneously.

This paper presents a state observer for an elastic joint with nonlinear friction via the information from an acceleration sensor. In order to avoid discontinuities, the nonlinear friction of the motor, which includes static, coulomb, and viscous terms, is considered a smooth function. In addition, it uses an acceleration sensor to obtain the information about the link with high uncertainty. The proposed state observer guarantees that the estimation error for the position and velocity of the link connected via an elastic joint containing a nonlinear stiffness (elasticity) converges to zero. In addition, it is shown that the observer gain can be designed by LMI (linear matrix inequality) optimization. Finally, to verify the performance of the proposed observer, the method proposed in this paper is tested by experiments on a two-inertia system with an elastic shaft.

The most commonly used part in engineering fields is threaded fasteners. There are a lot of advantages of fasteners. One of them is that they can be easily disassembled and reused, but a bolted joint can loosen easily when a transversal load is applied. The clamp load of a bolted joint can also loosen slowly when subjected to repeated temperature changes. This paper presents an experimental investigation of the self-loosening of bolted joints under cyclical temperature variation. Experiments are carried out under several cyclical temperature changes with different bolt preloads. Rectangular threaded bolted joints with M12 × 1.75 bolts and nuts are tested in a specially designed testing apparatus. Material of bolt, nut, and plates is a stainless steel. The experimental results show that the high initial bolt preload may prevent the joint from self-loosening and the bolted joint has loosened significantly in the first cycle of temperature changes. From this investigation, the loosening of the bolted joint can be considered as a first stage self-loosening.

The unique ability of retroviruses to integrate genes into host genomes is of great value for long-term expression in gene therapy, but only when integrations occur at safe genomic sites. To reap the benefit of using retroviruses without severe detrimental effects, we developed several murine leukemia virus (MLV)-based gammaretroviral vectors with safer integration patterns by perturbing the structure of the integrase via insertion of DNA-binding zinc-finger domains (ZFDs) into an internal position of the enzyme. ZFD insertion significantly reduced the inherent, strong MLV integration preference for genomic regions near transcriptional start sites (TSSs), which are the most dangerous spots. The altered retroviral integration pattern was related to increased formation of residual primer-binding site sequences at the 3′ end of proviruses. Several ZFD insertion mutants showed lower frequencies of integrations into the TSS genome regions when having the residual primer-binding site sequences in the proviruses. Our findings not only can extend the use of retroviruses in biomedical applications, but also provide a glimpse into the mechanisms underlying retroviral integration. Keywords: gammaretroviral vectors, DNA-binding protein domain, integrase, primer-binding site, transcriptional start sites

Three-dimensional printing can bring huge advantages when fabricating difficult structures and components in industry. This technology is also used commonly for printing threaded fasteners in the engineering field. The main disadvantage of threated fasteners is self-loosening. In this study, an International Organization for Standardization (ISO) standard M12 × 1.75 bolt and nut were printed by using a 3D printer and three types of 3D printing materials: acrylonitrile butadiene styrene (ABS-2), poly lactic acid (PLA), and glass. Following this, a test system for self-loosening of the 3D-printed bolts under cyclical temperature variations was constructed, and self-loosening of the bolted joints was observed by measuring the bolt preload using a load cell and the rotation displacement of the nut using a dial indicator according to temperature changes (from 10 °C up to 40 °C and 80 °C). The experimental results show that the ABS-2 bolt has good performance in terms of self-loosening phenomena under cyclical temperature changes, while the PLA bolt has poor performance in low temperature changes. The glass bolt indicated the lowest performance in high temperature changes.

Since artificial intelligence (AI) was introduced into engineering fields, it has made many breakthroughs. Machine learning (ML) algorithms have been very commonly used in structural health monitoring (SHM) systems in the last decade. In this study, a vibration-based early stage of bolt loosening detection and identification technique is proposed using ML algorithms, for a motor fastened with four bolts (M8 × 1.5) to a stationary support. First, several cases with fastened and loosened bolts were established, and the motor was operated in three different types of working condition (800 rpm, 1000 rpm, and 1200 rpm), in order to obtain enough vibration data. Second, for feature extraction of the dataset, the short-time Fourier transform (STFT) method was performed. Third, different types of classifier of ML were trained, and a new test dataset was applied to evaluate the performance of the classifiers. Finally, the classifier with the greatest accuracy was identified. The test results showed that the capability of the classifier was satisfactory for detecting bolt loosening and identifying which bolt or bolts started to lose their preload in each working condition. The identified classifier will be implemented for online monitoring of the early stage of bolt loosening of a multi-bolt structure in future works.

3D printing provides the advantage of the capability to manufacture a wide range of geometric models without constraint and using various materials. Owing to the recent rapid development of 3D printing technologies, manufacturing accuracy has also been improved. Precisely 3D-printed parts are used in various industries such as medical, robotics, and manufacturing industries. In this study, the friction performance of 3D printed ball bearing is evaluated to verify its feasibility as an engineering part. Multi-jet printing (MJP) method is utilized to prepare the ball bearing specimen as it provides the advantages of straightforward manufacturing process and high surface quality. The test rig is designed and constructed to evaluate the friction performance under various rotating speed and radial load conditions. From the experiments, the friction performance and feasibility of 3D printed ball bearings are evaluated, and effective operating conditions of 3D printed bearings are recommended. The results in this study can be used as a reference to design ball bearings using 3D printing method. Keywords: Multi-jet printing, 3D printed bearing, Ball bearing, Friction performance

Abstract This study investigated the stick-slip characteristics of a magneto-rheological elastomer (MRE) against an aluminum plate. Herein, the MRE was manufactured, and a stick-slip tester was employed to evaluate the stick-slip performance of the MRE under different velocities and load conditions with and without a magnetic field. The fast Fourier transform (FFT) of the friction force of the stick-slip and the roughness of the aluminum plate surface were calculated to confirm the stick-slip phenomenon. After the tests, the wear surfaces were observed to evaluate the wear properties of the MRE regarding the stick-slip. Results showed that the stick-slip was smaller at lower velocity. At higher velocity, the reduction of the stick-slip under a magnetic field was more clearly observed. Moreover, the wear reduced with reduced stick-slip under a magnetic field.

The present study emphasized on the optimal design of a motorized prosthetic leg and evaluation of its performance for stair walking. Developed prosthetic leg includes two degrees of freedom on the knee and ankle joint designed using a virtual product development process for better stair walking. The DC motor system was introduced to imitate gait motion in the knee joint, and a spring system was applied at the ankle joint to create torque and flexion angle. To design better motorized prosthetic leg, unnecessary mass was eliminated via a topology optimization process under a complex walking condition in a boundary considered condition and aluminum alloy for lower limb and plastic nylon through 3D printing foot which were used. The structural safety of a developed prosthetic leg was validated via finite element analysis under a variety of walking conditions. In conclusion, the motorized prosthetic leg was optimally designed while maintaining structural safety under boundary conditions based on the human walking data, and its knee motions were synchronized with normal human gait via a PD controller. The results from this study about powered transfemoral prosthesis might help amputees in their rehabilitation process. Furthermore, this research can be applied to the area of biped robots that try to mimic human motion.

To analyze the relationship between climatic factors (monthly mean temperature and total precipitation) and tree-ring growths of Pinus densiflora from the central region of the Republic of Korea, more than 20 trees were sampled from three national parks. The tree-ring chronology of Mt. Bukhan covering the period of 1917–2016 was assessed, as well as that of Mt. Seorak across 1687–2017 and Mt. Worak across 1777–2017. After cross-dating, each ring-width series was double-standardized by first fitting a logarithmic curve and then a 50 year cubic spline. Climate-growth relationships were computed with bootstrap correlation functions. The result of the analysis showed a positive response from the current March temperature and May precipitations for tree-ring growth of Pinus densiflora. It indicates that a higher temperature supply during early spring season and precipitation during cambium activity are important for radial growths of Pinus densiflora from the central region in the Republic of Korea.

A multistage centrifugal pump is designed for pumping low-viscosity, highly volatile and flammable chemicals, including hydrocarbons, for high head requirements. The five-stage centrifugal pump consists of a double-suction impeller at the first stage followed by a twin volute. The impellers for stages two through five are single-suction impellers followed by diffuser vanes and return channel vanes. The analytical performance is calculated initially in the design stage by applying similarity laws to an existing scaled-down pump model designed for low flow rate applications. The proposed pump design is investigated using computational fluid dynamics tools to study its performance in design and off-design conditions for water as the base fluid. The design feasibility of the centrifugal pump is tested for other fluids, such as water at a high temperature and pressure, diesel and debutanized diesel. The pump design is found to be suitable for a variety of fluids and operating ranges. The losses in the pump are analyzed in each stage at the best efficiency point. The losses in efficiency and head are observed to be higher in the second stage than in other stages. The detailed flow behavior at the second stage is studied to identify the root cause of the losses. Design modifications are recommended to diminish the losses and improve the overall performance of the pump.

Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed of three major components: semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module to enable the robot to perform cognitive tasks. The SMF creates an environment database using Triplet Ontological Semantic Model (TOSM) and builds semantic models of the environment. The environment maps from these semantic models are generated in an on-demand database and downloaded in SIP and SAN modules when required to by the robot. The SIP module contains active environment perception components for recognition and localization. It also feeds relevant perception information to behavior planner for safely performing the task. The SAN module uses a behavior planner that is connected with a knowledge base and behavior database for querying during action planning and execution. The main contributions of our work are the development of the TOSM, integration of SMF, SIP, and SAN modules in one single framework, and interaction between these components based on the findings of cognitive science. We deploy our cognitive navigation framework on a mobile robot platform, considering implicit and explicit constraints for autonomous robot navigation in a real-world environment. The robotic experiments demonstrate the validity of our proposed framework.

This article proposes a robust localization algorithm by improving the accuracy of the source position, receiver position, and sound speed using the known position of an object in the sea. The accuracy of those three types of information greatly influences the performance of the target localization in bistatic sonar. Although the locations of the source and receiver can be obtained using the global positioning system, there are still some position errors due to the limitations of global positioning system–based positioning systems and tidal current. In addition, the speed of the sound in the sea changes with temperature. The target estimation error was analyzed mathematically in terms of the mentioned errors. An improved localization is proposed using the accurate positions of objects in the sea, which can compensate for the errors in the source position, receiver position, and sound speed. The proposed algorithm is compared with the conventional algorithm through computer simulations.

Live attenuated vaccine (LAV), mimicking natural infection, provides an excellent protection against microbial infection. The development of LAV, however, still remains highly empirical and the rational design of clinically useful LAV is scarcely available. Apoptosis and caspase activation are general host antiviral responses in virus-infected cells. Utilizing these tightly regulated host defense mechanisms, we present a novel apoptosis-triggered attenuation of viral virulence as a rational design of live attenuated vaccine with desired levels of safety, efficacy, and productivity. Mutant influenza viruses carrying caspase recognition motifs in viral NP and the interferon-antagonist NS1 proteins were highly attenuated both in vitro and in vivo by caspase-mediated cleavage of those proteins in infected cells. Both viral replication and interferon-resistance were substantially reduced, resulting in a marked attenuation of virulence of the virus. Despite pronounced attenuation, the viruses demonstrated high growth phenotype in embryonated eggs at lower temperature, ensuring its productivity. A single dose vaccination with the mutant virus elicited high levels of systemic and mucosal antibody responses and provided complete protection against both homologous and heterologous lethal challenges in mouse model. While providing a practical means to generate seasonal or pandemic influenza live vaccines, the sensitization of viral proteins to pathogen-triggered apoptotic signals presents a potentially universal, mechanism-based rational design of live vaccines against many viral infections.