Power Assistance Research Articles

A Survey of Applications With Combined BIM and 3D Laser Scanning in the Life Cycle of Buildings

The integration of building information model (BIM) with point cloud from 3-D laser scanner provides powerful assistance in a variety of applications in the life cycle of building. However, the adoption of BIM and 3-D laser scanning for applications in the whole life cycle of buildings has yet to be satisfactorily reviewed. There is a need to understand the current situation of applications with BIM and 3-D laser scanning and their combined methods. The authors summarized combined methods of BIM and 3-D laser scanning at first. Then. the work presented in this article provides summarization of applications in the life cycle of building. In addition, the impact of emerging technologies on applications also be discussed. In this review, articles were limited to those published over the last decade. Two patterns in the approach to integrate BIM and 3-D laser scanning are reviewed. Several domains of application namely construction progress tracking, building components quality control, construction site safety, structural health monitoring, rescue after the disaster, energy modeling and management and modeling for exist building are looked back. According to occurred time of these applications, this article divides them into two classes: applications in construction period and applications in maintenance period. Based on the observed limitations in the reviewed papers, the authors conclude some potential future research trend for these applications based emerging technology at the same time.

An overview on agricultural farm power and energy

Tillage, plant safety, planting, threshing, and harvesting equipment, as well as other stationary tasks like irrigation, threshers, sellers, cleaners, graders, and so on, all need farm electricity. Agriculture's direct and indirect energy needs may be separated. Direct energy requirements include land planning, planting, harvesting, irrigation, food production, post-harvest processing, storage, and transportation of agricultural outputs and inputs. Farm power is now used to assist in agriculture at various stages and to do comfort farming with the assistance of various farm power. The author of this article addressed farm power and energy in agriculture, as well as how farm power aids agriculture at various levels. The author also addressed human, mechanical, and animal power, as well as electrical, solar, and renewable energy in this review article. Farm electricity will be utilized in huge quantities in the future, which will benefit those working in agriculture.

ЕСТОНІЯ ЗА УМОВ КРИЗ КІНЦЯ ХХ – ПОЧАТКУ ХХІ ст.: ПОГЛЯД ІСТОРИКА

A review of steps taken by the Estonian Republic at the path of managing the crises of the late 1980s – early 1990s, as well as the global financial and economic crisis of 2008 р. and the coronacrisis of 2020. Lacking traditional resources, crises were regarded as windows of opportunity. Such opportunities were determined by the core trends of global progress from industrial to post-industrial society. Emphasized is the importance of the specifics of Estonian mentality to modernization, particularly, the capacities of self-organization and creativity. Displayed is Estonia’s success in transformation into an information society as a response to the challenge of the crises. European integration of Tallinn played an important part in overcoming the events of crises and subsequent successful development. The Baltic country’s experience is important to Ukraine that enjoys its powerful support and assistance.

Evaluation of the Power Assist Effect of Muscle Suit for Lower Back Support

In order to reduce the burden on the lower back, we have been developing a compact and lightweight “Muscle Suit” that uses McKibben artificial muscles, a type of soft pneumatic actuator. The Muscle Suit for lower back assist has been commercialized and sold over 13,000 units so far; the evaluation of the assist effect is a key point for further development and marketing of the Muscle Suit. In this study, we focus on the assistive effects of two Muscle Suit models: the standard model and the standalone model. The former generates assistive force actively using the actuator, but the latter equips a mechanism generating assistive force passively without the actuator. In the experiments, we first conducted surface electromyogram (sEMG) measurement to examine muscle usage in lifting motion of a heavy weight with or without the Muscle Suit assists. Besides, we estimated the assist force of the Muscle Suit from the perspective of muscle usage ratio. As the result, the evaluation of the overall muscle usage of all measured muscles revealed that the both models of the Muscle Suit reduced muscle usage. We then examined whether the dynamic length of body sways (DLNG) could be used as an indicator of fatigue progressed with performing the lifting motion repeatedly under assist or non-assist conditions. The experimental results confirmed that the reduction of physical fatigue by the Muscle Suit suppressed the change of DLNG value.

Mechanical Analysis of Lifting Motion for Power Assist Suit and Proposal of Motion Detection Method

Mechanical Analysis of Lifting Motion for Power Assist Suit and Proposal of Motion Detection Method

Development of a Power Assist Suit for Transfer Assistance Composed of Passive Actuators

Development of a Power Assist Suit for Transfer Assistance Composed of Passive Actuators

Development of Motor-less Power Assist Device for Indirect Hot-line Work using Mechanical Self-weight Compensation Mechanism

Development of Motor-less Power Assist Device for Indirect Hot-line Work using Mechanical Self-weight Compensation Mechanism

Realization of Power Assist Control of a Motion System by Using a Vibration System as Operator Intention Input Device

Realization of Power Assist Control of a Motion System by Using a Vibration System as Operator Intention Input Device

Acceleration Control Design of HEVs with Comfortability Evaluation based on IRL

Abstract For passenger cars, comfortability is an important issue to consider in powertrain control. However, it is not easy to take comfortability into account when designing a powertrain control strategy because of its subjective nature and difficulty in being quantified. This paper presents a solution by using the inverse reinforcement learning (IRL) method. An acceleration scenario of a hybrid electric powertrain is considered to show this design approach. With a sample acceleration profile scored by an expert evaluating module, a reward function is obtained by training an extreme learning machine. Using the analytical representation of comfortability, an estimation of distribution algorithm (EDA) is used to seek the optimal acceleration reference. Given the reference acceleration signal, the control law for the electric motors that provide power assistance during the acceleration phase is obtained by solving a optimal tracking control problem. A numerical example is shown to evaluate the design approach.

Ammonia production from biomass via a chemical looping–based hybrid system

Abstract Ammonia is an indispensable raw material in the chemical industry. A hybrid biomass conversion to ammonia system (HBCAS) is developed by means of the chemical looping process with the assistance of solar energy and wind power. The system consists of six modules: i) biomass gasification, ii) oxy-syngas combustion, iii) chemical looping air separation (CLAS), iv) chemical looping ammonia production (CLAP), v) power generation, and vi) water electrolysis. A simulation was conducted for feasibility analysis and parameter optimization using ASPEN Plus, focusing on the development of a coordinated distribution network of energy and materials. Multi-generation of NH3, N2, and H2 was achieved using biomass cascading. The thermally neutral requirements of HBCAS and the effects of the operating conditions of each module on the selectivities (MnO2, AlN, and NH3), product concentrations, production rates, and reactant conversions were comprehensively considered. The results indicate that ammonia selectivity of 79.36%, production rate of 34.1 kmol/h, and concentration of 65.65 vol% can be obtained under typical conditions with 1 kg/s biomass input, confirming the feasibility of the HBCAS and providing guidance for its use.

A wearable lightweight exoskeleton with full degrees of freedom for upper-limb power assistance

ABSTRACT In this paper, a novel wearable lightweight exoskeleton with full degrees of freedom for upper-limb power assistance is developed. It is controlled by surface electromyography(sEMG) signals of the agonist muscle alone and aims to provide 30% power assistance to the elbow and shoulder joints for the flexion/extension movement. In the exoskeleton, there are two wearable identical arms and a back support pack. Each arm consists of two active joints and four passive joints, in which the active joints implement the power assistance for flexion/extension movement of the elbow and shoulder joints, whereas the passive joints fully follow the wearer's other motions. Moreover, with a rotatable and telescopic mechanism, the joint axis of the elbow joint of the exoskeleton can be adjusted to coincide with the elbow joint for different wearers, and meanwhile the passive joints of the shoulder complex of the exoskeleton can guarantee its instantaneous center of rotation to coincide with that of the wearer. As a result, the developed exoskeleton eliminates the restriction of motion and then the discomfort of the wearer. In addition, an extended admittance control is introduced and the effectiveness of the power assistance is verified.

Solar Photovoltaic Assistance System Study for a Brazilian Light Rail Vehicle

Recently, much research is being developed to improve sustainability in the transportation sector due to its impact on carbon dioxide (CO2) production, climate change, and fossil fuel resources. Railways in the world have started to use solar power assistance to improve sustainability in the transportation sector. The Brazilian Company of Urban Trains operates a Brazilian Light Rail Vehicle (BLRV) for passenger transportation in Brazil. This article is a study on the potential of environmental and economic benefits of the installation of solar panels in the BLRV roof, operated in the urban metropolitan area of João Pessoa, to provide assistance on electric load. The results show that the installation of photovoltaic system in the roofs of the total fleet of BLRVs operated in João Pessoa railway can save about R$ 800,000.00 and reduce the emission of 540 tons of CO2 in 10 years.

Functional Evaluation of a Force Sensor-Controlled Upper-Limb Power-Assisted Exoskeleton with High Backdrivability.

A power-assisted exoskeleton should be capable of reducing the burden on the wearer’s body or rendering his or her work improved and efficient. More specifically, the exoskeleton should be easy to wear, be simple to use, and provide power assistance without hindering the wearer’s movement. Therefore, it is necessary to evaluate the backdrivability, range of motion, and power-assist capability of such an exoskeleton. This evaluation identifies the pros and cons of the exoskeleton, and it serves as the basis for its subsequent development. In this study, a lightweight upper-limb power-assisted exoskeleton with high backdrivability was developed. Moreover, a motion capture system was adopted to measure and analyze the workspace of the wearer’s upper limb after the exoskeleton was worn. The results were used to evaluate the exoskeleton’s ability to support the wearer’s movement. Furthermore, a small and compact three-axis force sensor was used for power assistance, and the effect of the power assistance was evaluated by means of measuring the wearer’s surface electromyography, force, and joint angle signals. Overall, the study showed that the exoskeleton could achieve power assistance and did not affect the wearer’s movements.

A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals

With the growth of the number of elderly and disabled with motor dysfunction, the demand for assisted exercise is increasing. Wearable power assistance robots are developed to provide athletic ability of limbs for the elderly or the disabled who have weakened limbs to better self-care ability. Existing wearable power-assisted robots generally use surface electromyography (sEMG) to obtain effective human motion intentions. Due to the characteristics of sEMG signals, it is limited in many applications. To solve the above problems, we design a long short-term memory (LSTM) neural network model based on human mechanomyography (MMG) signals to estimate the motion acceleration of knee joint. The acceleration can be further calculated by the torque required for movement control of the wearable power assistance robots for the lower limb. We detect MMG signals on the clothed thigh, extract features of the MMG signals, and then, use principal component analysis to reduce the features’ dimensions. Finally, the dimension-reduced features are inputted into the LSTM neural network model in time series for estimating the acceleration. The experimental results show that the average correlation coefficient ( R) is 94.48 ± 1.91% for the estimation of acceleration in the process of continuously performing under approximately π/4 rad/s. This approach can be applied in the practical applications of wearable field.

Machine Learning-Based Decision Support System for Early Detection of Breast Cancer

Abstract: Background: Breast cancer is one of the leading causes of death of women in the United States and also one of the most malignant cancer among women worldwide. Early, more accurate detection of breast cancer enables extended longevity at a reduced cost. Towards this, analyzing the available big data using tools, such as Machine learning-based decision support systems can improve the speed and accuracy of early detection of breast cancer. In this paper, we examined the prediction performance of various state-of-theart machine learning models and a decision support system based on these models that provided the predicted category along with a prediction confidence measure. Methods: The various machine learning (ML) algorithms applied include Decision Tree, Naïve Bayes, k-Nearest Neighbors (kNN) and Support Vector Machine (SVM). We also analyzed the effect of multiple feature selection approaches on the prediction performance. We used the Breast Cancer Wisconsin Dataset from Wisconsin Prognostic Breast Cancer (WPBC) with 569 digitized images of a fine needle aspirate (FNA) of breast mass and 10 realvalued feature information. The performance of the ML model was evaluated using the ten-fold cross-validation approach and also on a prediction set comprising of 20% data with the models trained on remaining 80% data. Sensitivity and Specificity were used as the primary measures of performance. Results: Among all five machine learning methods, SVM had the best performance. Except for the kNN algorithm, the performance of the other three algorithms, Logistic Regressions, Naïve Bayes and Decision Trees, were also quite close to SVM. The prediction performance of the decision support system was better than any individual ML model where the prediction confidence was “High” or “Medium”. Conclusion: We found that feature selection improved the performance and computation cost for all ML models. By building the ML-based decision support system with the optimal feature subset, the prediction performance for breast cancer can be improved to 96% which means it can provide powerful assistance to doctors and patinets. On the other hand, as the size of the data set increases, the processing of data with a lot of features can increase the computation cost as well as the possibility of classification errors. Key words: Breast cancer, Data analysis, Machine learning, Feature selection, Decision support system.

Superconductivity in two-dimensional η-Mo3C2 films

Two-dimensional (2D) superconductors have intriguing physical properties and abundant potential applications. Recently, 2D superconducting α-Mo2C and face-centered cubic Mo2C have been controllably prepared and they bring new viewpoints to carbon-based superconductivity. Although molybdenum carbides (Mo-Cs) have multiple crystalline stacking orders, there are still few structures reported for the lack of higher energy supply during growth. In this study, we report a two-step vapor deposition method to grow superconducting η-Mo3C2 films with different thicknesses, with the assistance of controllable plasma power. The grown η-Mo3C2 films show polycrystalline characteristics, but they still present superior superconductivity. The 3.0-nm-thick film has the superconducting transition temperature of 5.38 K , and its electrical performances follow truly 2D superconducting transitions. This study will not only exhibit a robust superconducting η-Mo3C2 ultrathin film, but also provides a convenient growth way to realize more carbide-based heterostructures for future device applications.

Development of Non-Wearing Type Pneumatic Power Assist Device – Basic Concept and Performance Evaluation –

In Japan, where aging is faster than ever, the shortage of a young labor force is a serious problem, especially in the nursing field to support care recipients and in the primary industrial field to support heavy labor. Hence, the use of power assist devices that mechanically reduce the burden on the body is drawing increasing attention. This study focuses on the lifting motion, which can be performed by two methods, the squat method and the stoop method; the former involves bending the knee and the latter involves using the waist. The squat method is recommended because the burden on the waist is lower than that in the case of the stoop method. Currently, many types of wearable power assist devices to reduce the burden on the waist have been developed; however, they are based on the stoop method because of their assist mechanism. In this study, we developed a non-wearing type pneumatic power assist device that allows the squat method. After describing the basic concept and assist mechanism, the support effects are confirmed through experiments.

Phenotypic plasticity and taxonomy of Cladophora gracilis (Griffiths) Kützing (Cladophorales, Chlorophyta) in the western Yellow Sea with the implication of its DNA barcode

Due to extensive morphological plasticity, the taxonomy of Cladophora species remains unclear. As one of the widely distributed species, C. gracilis was reported to hold many varieties, which make it difficult to identify the species. This study explored the morphology diversity of nine C. gracilis samples collected along the coast of western Yellow Sea. Some samples showed extremely varied characteristics, by which one cannot classify them correctly. Hence, 18S rDNA and nuclear ribosomal DNA internal transcribed spacer regions (ITS) sequences were employed to delimit species. For 18S rDNA, sequence similarity ranged from 99.6% to 100%. For ITS region, the similarity ranged from 98.7% to 100%. Molecular data strongly suggested that the morphologically heterogeneous samples were actually the same species. Characteristics comparison of the samples revealed that the taxonomy criteria including branching pattern and density, thallus color, height and texture varied widely, influenced by environmental conditions and age of alga. Besides, cell dimensions, as the relatively stable criterion, also exhibited intraspecific variance. Successful application of 18S rDNA and ITS sequences indicated that molecular method can be a powerful assistant as DNA barcodes to traditional morphology taxonomy.

A 3D Computer Vision-Guided Robotic Companion for Non-Contact Human Assistance and Rehabilitation.

With the rapid aging of the U.S. population, the mobility impairment is becoming a more and more challenging issue that affects a large number of individuals. The research presented in this paper aims at helping the mobility-challenged individuals with a novel robotic companion, which is a walker-type mobile robot capable of accompanying the human user and keeping user at the center for protection and possible power assistance. The robotic companion is equipped with a 3D computer vision system, which provides a unique capability of sensing the human-robot relative position/orientation without physical contact or the need for wearable sensors. As such, the robotic companion enables the user to walk freely with minimum disturbance to his/her normal gait, relieving the user from the physical and cognitive loads associated with the use of traditional assistive devices. For the development of the robotic companion, the authors designed and fabricated a low-cost, differentially steered mobile robotic platform, and also developed a unique image processing system to extract the position/orientation information from the 3D camera-captured images. Furthermore, an advanced motion control system was developed for the robotic companion, which provides novel solutions to the unique challenges such as sway reduction and noise reduction in digital differentiation. To quantify the performance, component and system-level experimentation was conducted, and the results demonstrated that robotic companion and its key components function as desired and the system is expected to reduce the user load and improve the user mobility in real-world scenarios.

A Development of the Rehabilitaion System using the Power Assist Hand

A Development of the Rehabilitaion System using the Power Assist Hand