Analisis Penanganan Kerusakan Jalan Menggunakan Metode Bina Marga dan Pavement Conditional Index (PCI) Pada Ruas Jalan Kabuh - Tapen

The Kabuh – Tapen thruway is included within the category of Course III Collector road type which may be a interfacing street between Kabuh and Tapen streets. In this ponder, the point was to get the esteem of the harm list along the Kabuh - Tapen street. as a work reference and sort of street harm dealing with. Analysts will utilize the PCI and Bina Marga 1990 strategies as a reference. From the examination of the condition of the Kabuh – Tapen street segment on unbending asphalt at STA. 0+000 – 7+340 based on the Asphalt Condition Record (PCI) strategy, a esteem with an normal of 99% is gotten, demonstrating that the asphalt is in exceptionally great condition. On the adaptable asphalt at STA. 7+340 – 9+234, an normal esteem of 40% is gotten, showing that the street asphalt is in a awful condition. The comes about of the Bina Marga strategy on unbending asphalts gotten a priority order esteem of 7 so that the street is included within the schedule support program, whereas on flexible asphalts a need arrange esteem of 3 is gotten, hence it is included within the street enhancement program

Field dependent-shear stress prediction of magnetorheological fluid using an optimum extreme learning machine model

Extreme learning machine (ELM) application to model the shear stress of magnetorheological (MR) fluids has superiority over the existing methods, such as Herschel-Bulkley. Although the shear stress has been successfully predicted, the hidden node numbers are too high reaching up to 10,000 that will hinder the application of the models. Furthermore, the existing works have tried to determine the hidden node number only by trial and error method. Therefore, this paper aims to reduce the hidden node number by employing the particle swarm optimization (PSO) considering the accuracy and the hidden node numbers. The ELM based-shear stress model was firstly defined by treating the magnetic field and shear rate as the inputs and shear stress as output. The objective function optimization method was then formulated to minimize the normalized error and the hidden node numbers. Finally, the proposed methods were tested at various ELM activation functions and samples. The results have shown that the platform has successfully reduced the hidden node numbers from 10,000 to 571 while maintaining the error of less than 1%. In summary, the proposed objective function for PSO optimization has successfully built the optimum shear stress model automatically

Material characterizations of gr-based magnetorheological elastomer for possible sensor applications: rheological and resistivity properties

Considering persistent years, many researchers continuously seek an optimum way to utilize the idea of magnetorheology (MR) materials to be practically used for everyday life, particularly concerning resistivity sensing application. The rheology and resistivity of a graphite (Gr)-based magnetorheological elastomer (Gr-MRE) were experimentally evaluated in the present research. Magnetorheological elastomer (MRE) samples were prepared by adding Gr as a new additive during MRE fabrication. The effect of additional Gr on the rheological and resistivity properties were investigated and compared with those of typical MREs without a Gr additive. Morphological aspects of Gr-MRE were characterized using field emission scanning electron microscopy (FESEM) and energy dispersive X-ray spectroscopy (EDX). Rheological properties under different magnetic fields were evaluated using a parallel-plate rheometer. Subsequently, the resistivity of all samples was measured under different applied forces and magnetic fields. From the resistivity evaluation, two relationship curves resistance (R) under different applied forces (F) and different magnetic fields (B) were established and plotted by using an empirical model. It was observed from the FESEM images that the presence of Gr fractions arrangement contributes to the conductivity of MRE. It was also observed that, with the addition of Gr, rheological properties such as the field-dependent modulus can be improved, particularly at low strain amplitudes. It is also demonstrated that the addition of Gr in MRE can contribute to the likely use of force detection in tactile sensing devices

Effect of Magnetorheological Grease’s Viscosity to the Torque Performance in Magnetorheological Brake

Recently, magnetorheological grease (MRG) has been utilized in magnetorheological (MR) brakes to generate a braking torque based on the current applied. However, the high initial viscosity of MRG has increased the off-state torque that led to the viscous drag of the brake. Therefore, in this study, the off-state viscosity of MRG can be reduced by the introduction of dilution oil as an additive. Three samples consist of pure MRG (MRG 1) and MRG with different types of dilution oil; hydraulic (MRG 2) and kerosene (MRG 3) were prepared by mixing grease and spherical carbonyl iron particles (CIP) using a mechanical stirrer. The rheological properties in the rotational mode were examined using a rheometer and the torque performances in MR brake were evaluated by changing the current of 0 A, 0.4 A, 0.8 A, and 1.2 A with fixed angular speed. The result shows that MRG 3 has the lowest viscosity which is almost 93% reduction while the viscosity of MRG 2 has lowered to 25%. However, the torque performances generated by MRG 3 were highest, 1.44 Nm, when 1.2 A of current was applied and followed by MRG 2 and MRG 1. This phenomenon indicated that the improvement of torque performances was dependent on the viscosity of MRG. By reducing the viscosity of MRG, the restriction on CIP to form chain formation has also decreased and strengthen the torque of MRG brake. Consequently, the utilization of dilution oil in MRG could be considered in MR brake in near future

Electric Smart Solar Car System Based on Android

Along with the increasing number of motorized vehicles resulting in high pollution, energy efficient cars are needed. solar electric car is one of the car solutions fueled by henamt energy. the use of electric cars is considered more effective, in addition to reducing the use of petroleum fuels, it also does not cause pollution. This research makes solar electric cars using photovoltaic modules, electric cars and batteries. solar cell is a source of electrical energy to drive a DC motor supplied from batteries / batteries. while the battery is a storage place for electrical energy. The charge controller is a tool that functions to control the process of storing electrical power in the battery, the process of using the battery as a source of supplying electrical loads and monitoring the condition of the battery level during the charging and discharging process. for the operation of electric cars, android is equipped with automatic control of voice commands. The result of this research is a solar-powered electric car model that uses voice commands as a steering wheel

Improving passive ankle foot orthosis system using estimated ankle velocity reference

This study aims to investigate an appropriate ankle velocity reference (!ref), which is the average ankle velocity of a certain healthy subject when walking with a certain walking speed. The goal is to improve a Passive Controllable Ankle Foot Orthosis (PICAFO) by implementing the ankle velocity reference. Firstly, the function to estimate !ref; based on walking speed and body mass index (BMI), is obtained from 16 able-bodied subjects walking gait data. The effect of controlled stiffness (based on !ref) to the user's ankle kinematics and muscle activity was evaluated by comparing it to other settings, such as walking barefooted and various constant damping stiffness (0%, 30%, 60%, and 100% of the maximum damping stiffness). Two able-bodied subjects (normal and overweight) participated in the evaluation, where they had to walk at two different walking speeds (1 and 2 km/h). The result showed that ankle kinematics and muscle activity were improved when ! was controlled during walking speed of 1 km/h for both subjects. In terms of ankle kinematics, the toe clearance occurred, and walking stability increased. In terms of muscle activity, the average muscle activity had reduced by -29% for the normal subject and by -57% for an overweight subject, which shows that PICAFO provides necessary damping stiffness to replace the muscle works partially. In the future, by using the !ref based on walking speed and BMI, the therapists can skip the trial and error process of finding an appropriate PICAFO prescription for a post-stroke patient

Sensor number optimization using neural network for ankle foot orthosis equipped with magnetorheological brake

A passive controlled ankle foot orthosis (PICAFO) used a passive actuator such as Magnetorheological (MR) brake to control the ankle stiffness. The PICAFO used two kinds of sensors, such as Electromyography (EMG) signal and ankle position (two inputs) to determine the amount of stiffness (one output) to be generated by the MR brake. As the overall weight and design of an orthotic device must be optimized, the sensor numbers on PICAFO wanted to be reduced. To do that, a machine learning approach was implemented to simplify the previous stiffness function. In this paper, Non-linear Autoregressive Exogeneous (NARX) neural network were used to generate the simplified function. A total of 2060 data were used to build the network with detail such as 1309 training data, 281 validation data, 281 testing data 1, and 189 testing data 2. Three training algorithms were used such as Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The result shows that the function can be simplified into one input (ankle position)-one output (stiffness). Optimized result was shown by the NARX neural network with 15 hidden layers and trained using Bayesian Regularization with delay 2. In this case, the testing data shows R-value of 0.992 and MSE of 19.16

Electric Smart Solar Car System Based on Android

Along with the increasing number of motorized vehicles resulting in high pollution, energy efficient cars are needed. solar electric car is one of the car solutions fueled by henamt energy. the use of electric cars is considered more effective, in addition to reducing the use of petroleum fuels, it also does not cause pollution. This research makes solar electric cars using photovoltaic modules, electric cars and batteries. solar cell is a source of electrical energy to drive a DC motor supplied from batteries / batteries. while the battery is a storage place for electrical energy. The charge controller is a tool that functions to control the process of storing electrical power in the battery, the process of using the battery as a source of supplying electrical loads and monitoring the condition of the battery level during the charging and discharging process. for the operation of electric cars, android is equipped with automatic control of voice commands. The result of this research is a solar-powered electric car model that uses voice commands as a steering wheel

A Review on the Control of the Mechanical Properties of Ankle Foot Orthosis for Gait Assistance

In the past decade, advanced technologies in robotics have been explored to enhance the rehabilitation of post-stroke patients. Previous works have shown that gait assistance for post-stroke patients can be provided through the use of robotics technology in ancillary equipment, such as Ankle Foot Orthosis (AFO). An AFO is usually used to assist patients with spasticity or foot drop problems. There are several types of AFOs, depending on the flexibility of the joint, such as rigid, flexible rigid, and articulated AFOs. A rigid AFO has a fixed joint, and a flexible rigid AFO has a more flexible joint, while the articulated AFO has a freely rotating ankle joint, where the mechanical properties of the AFO are more controllable compared to the other two types of AFOs. This paper reviews the control of the mechanical properties of existing AFOs for gait assistance in post-stroke patients. Several aspects that affect the control of the mechanical properties of an AFO, such as the controller input, number of gait phases, controller output reference, and controller performance evaluation are discussed and compared. Thus, this paper will be of interest to AFO researchers or developers who would like to design their own AFOs with the most suitable mechanical properties based on their application. The controller input and the number of gait phases are discussed first. Then, the discussion moves forward to the methods of estimating the controller output reference, which is the main focus of this study. Based on the estimation method, the gait control strategies can be classified into subject-oriented estimations and phase-oriented estimations. Finally, suggestions for future studies are addressed, one of which is the application of the adaptive controller output reference to maximize the benefits of the AFO to users

Control reference parameter for stance assistance using a passive controlled ankle foot Orthosis - a preliminary study

This paper aims to present a preliminary study of control reference parameters for stance assistance among different subjects and walking speeds using a passive-controlled ankle foot orthosis. Four young male able-bodied subjects with varying body mass indexes (23.842 ± 4.827) walked in three walking speeds of 1, 3, and 5 km/h. Two control references, average ankle torque (aMa), and ankle angular velocity (aω), which can be implemented using a magnetorheological brake, were measured. Regression analysis was conducted to identify suitable control references in the three different phases of the stance. The results showed that aω has greater correlation (p) with body mass index and walking speed compared to aMa in the whole stance phase (p1(aω) = 0.666 > p1(aMa) = 0.560, p2(aω) = 0.837 > p2(aMa) = 0.277, and p3(aω) = 0.839 > p3(aMa) = 0.369). The estimation standard error (Se) of the aMa was found to be generally higher than of aω (Se1(aMa) = 2.251 > Se1(aω) = 0.786, Se2(aMa) = 1.236 > Se2(aω) = 0.231, Se3(aMa) = 0.696 < Se3(aω) = 0.755). Future studies should perform aω estimation based on body mass index and walking speed, as suggested by the higher correlation and lower standard error as compared to aMa. The number of subjects and walking speed scenarios should also be increased to reduce the standard error of control reference parameters estimation