Hasil untuk "Engineering machinery, tools, and implements"

Memoona Farooq, Chih-Yuan Chen, Cheng-Pin Wang

Watermelon is a popular fruit, predominantly cultivated in Asian countries. However, the production and harvesting processes present several challenges. Due to its size and weight, manually harvesting watermelons is labor-intensive and costly. In the future, technology is expected to enable robots to harvest watermelons. Therefore, it becomes essential to introduce intelligent systems to effectively identify and locate watermelons in harvesting. This research aims to develop an advanced methodology for watermelon identification and location using You Look Only Once (YOLO)v8 and YOLOv8-oriented bounding box (OBB) algorithms. Furthermore, the simple online and real-time tracking (SORT) algorithm was employed to track and count watermelons and estimate yield. The performance of YOLOv8-OBB was better than that of YOLOv8 and the highest precision (0.938) was achieved by YOLOv8s-OBB. Additionally, the size of each watermelon was measured with both models. The models help farmers find the optimal watermelons for harvest.

Masahiro NOMOTO, Yuji IWAMATSU, Tomonobu SATO et al.

Aerators are necessary for the conservation of water quality in dams, as they increase water circulation and oxygenate the water. To estimate aerator performance characteristics in the field, the optimal spatial range, resolution, and sampling time interval need to be determined for the targeted water mass. In this study, we conducted precise field experiments at Hiyoshi Dam in Kyoto, Japan, by employing a new type of aerator in the summer of 2012. Initially, the water temperature distribution near one aerator was observed using an automatic temperature profiler capable of horizontal movement on the water surface over a distance of 90 m at a speed of 0.1 m/s during a sampling cycle of 15 minutes. The vertical temperature distribution was determined by a series of thermocouples suspended in steps of 0.1 to 1 m on a 10-m-long cable beneath the surface. Subsequently, the water temperature distribution between two identical aerators located 275 m apart was observed using 6 pairs of vertically suspended thermocouples connected horizontally with a cable at 25-m intervals. Finally, the water flow distribution near one aerator was observed with an electromagnetic flowmeter. Analysis of the results clearly showed how an entrained water mass intruded into a thermocline that formed at a depth of 2.5 m. In addition, details regarding the speed and flow rate of the intrusion, the mixing efficiency, and the estimated number of days required for shallow layer circulation were estimated.

Gerardo Covarrubias, Xuedong Liu

This research aimed to elucidate the methodology employed in econometric estimations by utilizing dichotomous variables. These variables served a dual purpose: firstly, they denoted an attribute designed to discern structural changes within a linear relationship, and secondly, they quantified the impact and statistical significance of one or more quantitative independent variables on a dependent variable in the presence of such structural changes. The core of this document covers the presentation of the methodology; additionally, an applied analysis of Mexico’s foreign trade is included. This analysis delved into estimating the impact and statistical significance of exports on the economic growth across different periods, reflecting significant structural changes in the development of the Mexican economy.

Yusuke MORITA, Yoji YAMADA

In building a human-robot cooperative system, it is an important issue to reduce the harm caused by contact with the robot. A protective measure for a robot with a sharp and highly rigid surface is to smoothen the surface shape and reduce the rigidity in order to increase the contact area, and protection with a soft covering is effective. In this research, we focus on the event when the edge of the robot contacts the human body, especially the outer arms or legs. In order to design an appropriate soft covering, it is necessary to estimate the contact pressure between each part of the human body and the edge covered with the soft covering. The contact pressure can be derived from a contact model that considers the curved surface shape of the human body, the radius of curvature of the soft covering and edges, and the stress balance that considers the elastic modulus that hardens the hardness of the human body and the soft covering according to the compressive strain. Then, the validity of the proposed method was confirmed by material experiments to grasp the material properties and experiments simulating contact, using appropriate materials for the contact model targeted in this research. The proposed method can contribute to understanding the tendency of contact pressure by arbitrarily estimating the curvature radius of the edge and the hardness and thickness of the protective covering that covers the surface when applying the soft covering to the edge of the robot surface.

Muhammad Mahad Shah, Arslan Ahmed, Usman Latif et al.

Piezoelectric energy harvesters are considered a replacement for batteries because they are self-sustainable with low or no maintenance, and suitable for off-grid devices. In this study, two cylinders were placed in a flowing stream of water. The upstream cylinder of 25 mm diameter is fixed at the center of the stream while another cylinder of the same diameter, which is free to oscillate in the crosswise direction of the flow and is placed in the wake of that fixed cylinder. A piezoelectric flag is then placed in the wake of the vibrating cylinder and its distance from the vibrating cylinder was changed to observe the effect of wake length on the energy harvester. The energy harvesting potential of the system is explored for flow velocities ranging from 0.28 m/s to 0.36 m/s. The distance between the cylinders Gx, which is explored for the range of 2.5D–7D, and the distance between the piezoelectric membrane and the vibrating cylinder are expressed as Sx for the same range. The maximum power of 19.17 μW was produced at Sx = 2D and Gx = 3.5D. The maximum power for the baseline case, when two tandemly arranged stationary circular cylinders were used and a piezoelectric membrane was placed in their wake, is 10.24 μW; hence, an increase of 87.2% is observed under the same ambient conditions.

Ahmad Abubakar Suleiman, Arsalaan Khan Yousafzai, Muhammad Zubair

Ensuring access to safe drinking water is a critical concern, particularly in regions with limited resources. This study evaluates groundwater potability using a range of machine learning models, including logistic regression, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and Random Forest, as well as deep learning models such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Feedforward Neural Networks (FNNs), and Long Short-Term Memory (LSTM). We collected thirty groundwater samples from residential and industrial locations in Jaen, Kano State, Nigeria, focusing on nine crucial physicochemical parameters: electric conductivity, pH, total dissolved solids, calcium, magnesium, chloride, zinc, manganese, and copper. Machine learning models, such as Logistic Regression and Random Forest, achieved accuracy scores of 0.833. They were closely followed by deep learning models, such as ANNs, with an accuracy score of 0.833, and LSTM, which scored 0.666. KNN and SVC provided moderately accurate predictions, scoring 0.667, while CNN and FNN achieved lower scores of 0.333 and 0.5, respectively. This study represents a significant step toward ensuring safe drinking water for communities and preserving the sustainability of natural resources.

Shu-Mei Chao, Mu-Jin Chen

The purpose of this study is to evaluate the research efficiency of 40 departments in a university from 2015 to 2017. In this study, data envelopment analysis (DEA) is used with a non-parametric mathematical linear planning approach, and an appropriate model is proposed for evaluating the research efficiency of the university’s departments. The analyzed items are selected based on the relevant literature on research efficiency. The result of this study helps research policymakers and motivates faculty and researchers to take the initiative for better-quality research with limited resources and international competitiveness.

Ejay Nsugbe, Halin Buruno, Stephanie Connelly et al.

The ranking and evaluation of a surgeon’s surgical skills is an important factor in order to be able to appropriately assign patient cases according to the necessary level of surgeon competence in addition to helping us in the process of pinpointing the specific clinicians within the surgical cohort who require further developmental training. One of the more frequent means of surgical skills evaluation is through a qualitative assessment of a surgeon’s portfolio alongside other supporting pieces of information, a process which is rather subjective. The contribution presented as part of this paper involves the use of a set of Delsys Trigno EMG wearable sensors, which track and record the muscular activation patterns of a surgeon during a surgical procedure, alongside computationally driven artificial intelligence (AI) methods towards the differentiation and ranking of the surgical skills of a clinician in a quantitative fashion. The participants in the research involved novice-level surgeons, intermediate-level surgeons and expert-level surgeons in various simulated surgical cases. A comparison of different signal processing approaches has shown that the proposed approach can prove beneficial in monitoring and differentiating the skillsets of various surgeons for various kinds of surgical cases. The presented method could also be used to track the evolution of the surgical competencies of various trainee surgeons at various stages during their training.

Majid Yazdani, Clément Pot, Quentin Boyadjian et al.

To illustrate how the texture evolution of a polycrystalline aluminum alloy is dependent on the deformation path, hot extruded AA6082 plates in T6 conditions were rolled with various deformation modes. Unidirectional and cross-rolling were conducted at room temperature for different levels of thickness reduction. The resulting textures were then evaluated using the electron backscatter diffraction technique. Depending on the extent of deformation, different textures were obtained. The strong texture of the initial hot extruded material influences texture development and provides new insights into texture development in aluminum alloys. The evolution of the Cube component was particularly interesting in this matter. A competition between dislocation glide and crystal rotation could explain the observed results. The comprehension of these mechanisms leads to a better understanding of texture evolution that drives many properties in aluminum alloys.

Taichi MAEDA, Keiichi WATANUKI

In the manufacturing industry, the transfer of knowledge of expert is an issue because the number of experts is decreasing. In order to support knowledge transfer, various support systems have been developed. However documenting knowledge and determining the level of system users is a concern. This study aims to develop a technique for knowledge extraction and user level judgement using gaze measurement. In order to develop these techniques, the difference of gaze between expert and beginner need to be evaluated. In this study, the gaze of experts and beginners were measured in relation to the vortex exploration skill in the task to explore for vortices which is a typical phenomenon from fluid simulation results. 6 fluid simulation result pictures were presented to experts and beginners, and their gaze was measured. Our result suggested that the expert observe vortex area and area without vortex long time. Beginner did not observe the horizontal elliptical vortices many times, as a result they often missed it. Beginner’s gaze transfer between vortices area many times than expert’s. Expert’s pupil size was larger than beginner’s. These results imply that the experts observe the simulation result image widely, find a place where there is a possibility of a vortex, and concentrate long time to judge whether it is a vortex. On the other hand, the beginners find a place where there is a possibility vortex in the simulation result image and compare other area where there is a possibility vortex to judge whether it is a vortex. By utilizing the gaze measurements, the method of exploring the vortex of the experts was revealed and it was found that user’s level can be judged.

D. A. Makarenkov, A. N. Tsedilin

Civilizational development entails the formation of a contradiction between the technosphere and the biosphere, with the consequences of greenhouse effects; it is carried out within the framework of technological structures independent of socio-economic formations. Governing bodies and activities that provide significant growth (or control planetary environmental safety) are key factors, as are technological innovations, with industry and consumption playing a key role in the spread of a new technological order. The technosphere, as the main product of civilizational development, has gone through a number of pre-industrial and at least five industrial technological modes and will most likely move into the sixth (post-industrial) technological mode. The main causes of greenhouse gases are gas emissions, methane, and ozone. The rise in the 20th century of the temperature on the surface of the planet is a result of a reaction to anthropogenic emissions of gases, including accidental oxides, tropospheric ozone and its “expectations”, halogenated hydrocarbons, nitrogen oxides, etc.

Elisabetta Bodo, Valentina Bello

In this work, we present a compact micro-opto-fluidic sensing platform for the measurement of volumetric refractive index (RI) variations of ultra-low volumes of fluids with respect to a reference liquid. In the instrumental configuration, we employed a disposable plastic micro-channel, which was customized with integrated back and front aluminum reflectors, deposited by sputtering. The presence of the double metallization is exploited to create a zigzag guiding path for the radiation provided by a semiconductor laser diode, so that light crosses the fluid under test multiple times before reaching a 1-D Position Sensitive Detector (PSD). According to Snell law, when fluids with different RI indices fill the channel, the radiation is deflected at different angles and the output beam shifts along the channel surface. RI variations are monitored by measuring the position of the output light spot on the surface of the PSD. To validate the results, a theoretical model based on ray optics was developed to study the propagation of the radiation travelling through the fluidic channel. Experimental results showed a beam displacement per RI unit up to 3234 μm/RIU, in agreement with the prediction of the analytical model. The proposed sensing method is label-free, contactless, non-invasive, and biologically safe. Moreover, the micro-opto-fluidic sensing platform could be exploited in a wide range of applications, ranging from biology to medicine to the agri-food industry.

Keiichi HIROAKI, Daiki KATOU, Masahiro WATANABE

This paper describes vibration characteristics of a cantilevered flexible sheet under the periodic aerodynamic excitation. In the experiment, movable plates(exciter) are set at the upstream side from the sheet in a wind-tunnel. Moreover, a theoretical model for calculation of a frequency response (amplitude ratio between the movable plate and sheet) subjected to aerodynamic excitation by the movable plate is developed. Fluid force acting on the sheet surface is calculated by the Doublet-point Method based on the unsteady lifting surface theory. Finally, the local work done by the fluid force acting on the sheet surface under aerodynamic excitation is determined theoretically and the region in which the exciting fluid force acts is clarified.

Kalinović Saša M., Djoković Jelena M., Nikolić Ružica R. et al.

Thermal fracture characteristics – the thermal energy release rate and thermal stress intensity factor of a semi-infinite crack at an interface between the two elastic isotropic materials, subjected to the temperature variations, are considered in this paper. Those characteristics are determined based on application of the linear elastic fracture mechanics (LEFM) concept. Expressions for obtained theoretical solutions are compared to solutions from literature and they are found to be more concise. Influence of the materials change on these two thermal fracture properties were observed, as well as the influence of the thickness ratio of the two layers constituting the interface.

Hiroaki T.-KANEKIYO, Shinjiro AGATA

An inventory management problem is theoretically discussed for a factory having effects of lead times in replenishing the inventory, where it stocks materials used for its products. It is assumed that the factory can dynamically control the size of ordering materials. By applying the stochastic control theory, the optimal control of the ordering size is derived, in which the expected total cost up to an expiration time is minimized. First, a new stochastic model is constructed for describing an inventory fluctuation of the factory by the use of a non-diffusive stochastic differential equation, where an analytic time is introduced so that the inventory process can be a Markov process even though it is affected by lead times. Next, an optimal control is formulated by introducing an evaluation function quantifying total costs. Based upon them, the Hamilton-Jacobi-Bellman (HJB) equation is derived, whose solution gives the optimal control. Finally, the optimal control is quantitatively examined through numerical solutions of the HJB equation. Numerical results indicate that if time up to an expiration time is short then the optimal control is affected by it, otherwise, the optimal control does not depend on it.

Linjiang Wu, Chao Liu, Tingting Huang et al.

Accurate traffic sensor data is essential for traffic operation management systems and acquisition of real-time traffic surveillance data depends heavily on the reliability of the traffic sensors (e.g., wide range detector, automatic traffic recorder). Therefore, detecting the health status of the sensors in a traffic sensor network is critical for the departments of transportation as well as other public and private entities, especially in the circumstances where real-time decision is required. With the purpose of efficiently determining the sensor health status and identifying the failed sensor(s) in a timely manner, this paper proposes a graphical modeling approach called spatiotemporal pattern network (STPN). Traffic speed and volume measurement sensors are used in this paper to formulate and analyze the proposed sensor health monitoring system and historical time-series data from a network of traffic sensors on the Interstate 35 (I-35) within the state of Iowa is used for validation. Based on the validation results, we demonstrate that the proposed approach can: (i) extract spatiotemporal dependencies among the different sensors which leads to an efficient graphical representation of the sensor network in the information space, and (ii) distinguish and quantify a sensor issue by leveraging the extracted spatiotemporal relationship of the candidate sensor(s) to the other sensors in the network.

Takuya IGUCHI, Yuki YAMAKAWA, Koichi HASHIGUCHI et al.

This paper presents a finite strain elastoplastic constitutive model incorporating the extended subloading surface concept within the unconventional plasticity framework for cyclic loadings. This is a reformulated and extended version of the small strain model [Iguchi et al., Trans. JSME (in Japanese), Vol. 82 (2016), No. 841 p. 16-00197]. The constitutive formulation is underpinned by the multiplicative decomposition of the deformation gradient tensor, which is the well-established modern kinematical framework in geometrically nonlinear elastoplasticity. In addition to the conventional multiplicative decomposition into elastic and plastic parts, we further introduce two kinds of multiplicative decompositions of the plastic deformation gradient tensor. One decomposition is related to kinematic hardening, and the other an evolution of the elastic-core tensor, i.e. a key internal variable in the extended subloading surface model, which stands for a stress state where the material exhibits most elastic responses. In each decomposition, the plastic deformation gradient tensor is split into an energetic part and a dissipative part, and the former is related to the back-stress tensor for kinematic hardening or the elastic-core tensor defined on a pertinent intermediate configuration via a hyperelastic format. Therefore, the whole constitutive theory can be formulated in terms of deformation-like tensorial variables without resort to any objective or co-rotational rates of stress-like variables such as the back-stress, fulfilling the principle of material frame indifference. We then focus on the numerical stress update scheme for the proposed material model based on the fully implicit return-mapping. Basic properties of the proposed model, as well as the capability of the developed numerical scheme, are examined and verified through numerical examples.

Jay Lee, David Siegel, Wenyu Zhao et al.

As wind energy proliferates in onshore and offshore applications, it has become significantly important to predict wind turbine downtime and maintain operation uptime to ensure maximal yield. Two types of data systems have been widely adopted for monitoring turbine health condition: supervisory control and data acquisition (SCADA) and condition monitoring system (CMS). Provided that research and development have focused on advancing analytical techniques based on these systems independently, an intelligent model that associates information from both systems is necessary and beneficial. In this paper, a systematic framework is designed to integrate CMS and SCADA data and assess drivetrain degradation over its lifecycle. Information reference and advanced feature extraction techniques are employed to procure heterogeneous health indicators. A pattern recognition algorithm is used to model baseline behavior and measure deviation of current behavior, where a Self-organizing Map (SOM) and minimum quantization error (MQE) method is selected to achieve degradation assessment. Eventually, the computation and ranking of component contribution to the detected degradation offers component-level fault localization. When validated and automated by various applications, the approach is able to incorporate diverse data resources and output actionable information to advise predictive maintenance with precise fault information. The approach is validated on a 3 MW offshore turbine, where an incipient fault is detected well before existing system shuts down the unit. A radar chart is used to illustrate the fault localization result.

Hideto NISHIMOTO, Ryutaro TANAKA, Akira HOSOKAWA et al.

In this paper, the measurement method of tool edge temperature using a two-color pyrometer with an optical fiber in wet cutting is proposed. Using the proposed method, the high pressure air supplied from the small hole where an optical fiber is inserted prevents coolant from adhering to the optical fiber edge and makes temperature measuring possible. The influence of this supplied air pressure on the tool edge temperature is negligible in the 0.05Mpa to 0.6MPa range. In this way, the temperature of CBN and PCD tool edge is measured in wet cutting of Ti-6Al-4V. As a result, the tool edge temperature increases rapidly with the increase of cutting speed. In dry cutting, PCD tool edge temperature is almost 100 °C lower compared with CBN tool edge temperature. By coolant supply, PCD tool edge temperature decreases greatly. For example, when cutting speed is set at 300m/min, PCD tool edge temperature decreases by 350°C, while CBN tool edge temperature decreases by only 70°C.

Kazuhiko TAKAHASHI, Motoki KUROKAWA, Masafumi HASHIMOTO

This paper investigates a quantum neural network and discusses its application in control systems. A learning-type neural network-based controller that uses a multi-layer quantum neural network having qubit neurons as its information processing unit is proposed. Three learning algorithms; a back-propagation algorithm, a conjugate gradient algorithm and a real-coded genetic algorithm, are investigated to supervise the training of the multi-layer quantum neural network. To evaluate the learning performance and the capability of the quantum neural network-based controller, we conducted computational experiments for controlling a nonlinear discrete-time plant and a nonholonomic system - in this study a two-wheeled robot. The results of computational experiments confirm both the feasibility and the effectiveness of the quantum neural network-based controller and that the real-coded genetic algorithm is suitable for the learning method of the quantum neural network-based controller.