In this paper, we describe an extended version of Voxento which is an interactive voice-based retrieval system for lifelogs that has been developed to participate in the fourth Lifelog Search Challenge LSC'21, at ACM ICMR'21. Voxento provides a spoken interface to the lifelog dataset, which facilitates a novice user to interact with a personal lifelog using a range of vocal commands and interactions. For the version presented here, Voxento has been enhanced with new retrieval features and better user interaction support. In this paper, we introduce these new features, which include dynamic result filtering, predefined interactive responses and the development of a new retrieval API. Although Voxento was proposed for wearable technologies such as Google Glass or interactive devices like smart TVs, the version of Voxento presented here uses a desktop computer in order to participate in the LSC'21 competition. In the current Voxento iteration, the user has the option to enable voice interaction or use standard text-based retrieval.

https://dl.acm.org/doi/pdf/10.1145/3379172.3391728

Voxento 2.0: A Prototype Voice-controlled Interactive Search Engine for Lifelogs

Nowadays, most hospitals have new problem that is lack of medical nurse due to the number of patient increas rapidly. The patient especially with physical disabilities are difficult to control the switch on electrical appliances in patient’s room. This research aims to develope voice recognition based home automation and being applied to patient room. A miniature of patient’s room are made to simulate this system. The patient's voice is received by the microphone and placed close to the patient to reduce the noise.V3 Voice recognition module is used to voice recognition process. Electrical bed of patient is represented by mini bed with utilising motor servo. The lighting of patient room is represented by small lamp with relay. And the help button to call the medical nurse is represented by buzzer. Arduino Uno is used to handle the controlling process. Six basic words with one syllable are used to command for this system. This system can be used after the patient's voice is recorded. This system can recognize voice commands with an accuracy 75%. The accuracy can be improved up to 85% by changing the voice command into two syllables with variations of vowels and identical intonation. Higher accuracy up to 95% can be reached by record all the subject’s voice.

Voice recognition system for controlling electrical appliances in smart hospital room

Optimization structures are mostly considered for resolving multi-objective difficulties similar to cost, emission, and financial load dispatch in various energy sources. Non-renewable energy sources (NRES) emit harmful gases like CO2, and methane. which results in air pollutants, so various techniques are used in survey papers. By considering optimization techniques, the multi-objective problems are reduced in renewable energy sources (RES) and NRES. Implementing these techniques in RES and NRES will define the proper objective function. Hybrid algorithms are used for solving multi-objective problems like cost, pollutant emission, price penalty factor, valve point, ramp rates, and constraints like generator, power flow, power balance, and heat balance. A fuzzy system is used in numerous surveys for controlling purpose, superiority, and efficiency over other controllers. Subsequently summarized three types of sources like RES, RES-NRES, and NRES for easy identification of techniques and problems. This study reviews various techniques and mathematical modeling of algorithms for future research.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/22179/14046

Various PSO methods investigation in renewable and nonrenewable sources

In the Indonesian Wheeled Football Robot Contest (KRSBI) wheeled division, the robot that is made must be able to complete a predetermined task, one of which is the robot for chasing the ball and catching it. Holonomic is one of the methods used in navigating the omnidirectional movement of mobile robot applications. Because the movement is designed without changing the position of the robot in the direction of the facing, the omnidirectional wheels are used which has the ability to move freely in two directions. The mobile robot has three omnidirectional wheels and DC motors each used for movement. DC motors controlled by EMS 30A H-Bridge as a driver and Arduino Mega 2560 as the main microcontroller. Holonomic and inverse kinematic calculations are conducted to control the mobile robot movement of x, y, and ω toward angular velocity and direction of s1, s2, and s3 for each wheel. The length of the wheel axis to the middle of the body of robot is 160 mm. In this study, a robot was implemented on the robot movement for moving forward, backward, sideways, and diagonal direction. Based on the data evaluation, it is determined that an angular error of 2.84% exists in the movement of the omnidirectional robot at a velocity of 0.256 m/s to 1.403 m/s.

/pdf/holonomic-implementation-of-three-wheels-omnidirectional-58xi0fbh6f.pdf

Holonomic Implementation of Three Wheels Omnidirectional Mobile Robot using DC Motors

Saybolt color is a standard measurement scale used to determine the quality of petroleum products and the appropriate refinement process. However, the current color measurement methods are mostly laboratory-based, thereby consuming much time and being costly. Hence, we designed an automated model based on an artificial neural network to predict Saybolt color. The network has been built with five input variables, density, kinematic viscosity, sulfur content, cetane index, and total acid number; and one output, i.e., Saybolt color. Two backpropagation algorithms with different transfer functions and neurons number were tested. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to assess the performance of the developed model. Additionally, the results of the ANN model are compared with the multiple linear regression (MLR). The results demonstrate that the ANN with the Levenberg–Marquart algorithm, tangent sigmoid transfer function, and three neurons achieved the highest performance (R2 = 0.995, MAE = 1.000, and RMSE = 1.658) in predicting the Saybolt color. The ANN model appeared to be superior to MLR (R2 = 0.830). Hence, this shows the potential of the ANN model as an effective method with which to predict Saybolt color in real time.

/pdf/a-neural-network-based-model-for-predicting-saybolt-color-of-11z9p1q0.pdf

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products

Photovoltaic (PV) is a source of electrical energy derived from solar energy and has a poor level of efficiency. This efficiency is influenced by PV condition, weather, and equipments like Maximum Power Point Tracking (MPPT). MPPT control is widely used to improve PV efficiency because MPPT can produce optimal power in various weather conditions. In this paper, MPPT control is performed using the Fuzzy Logic-Particle Swarm Optimization (FL-PSO) method. This FL-PSO is used to get the Maximum Power Point (MPP) and minimize the output power oscillation from PV. From the simulation results using FL-PSO, the values of voltage, and output power from the boost converter are 183.6 V, and 637.7 W, respectively. The ripple of output power from PV with FL-PSO is 69.5 W. Then, the time required by FL-PSO reaches MPP is 0.354 s. Compared with MPPT control based on the PSO method, the MPPT technique using FL-PSO indicates better performance and faster than the PSO.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/20544/13134

An improved control for MPPT based on FL-PSo to minimize oscillation in photovoltaic system

In the Automatic Sorting System, precision volume and size of the parcel boxes are all concerned. Different box size will certainly have a limited of personnel who visually identify and pick the objects. Computer vision are related to image processing and image analysis tend to focus on 2D image by pixel operation. Contour-based object detection is one alternative can measure the area for objects. In this paper will be in design automation development of a computer vision system that is able to get the volume of the parcel boxes. To get this this value if know the amount of length, width, and height. To find out the dimensional scale of parcel will be used two webcam cameras by calculating the pixels that are captured on camera and making comparison for calibration. The 2D image consists of two images from camera captured with vertical and horizontal view. After getting the length, width, and height of the parcel box, there will be a multiplication program is used to obtain the result of volume. It be separated automatically on the conveyor belt. The system can identify boxes to within 1 – 15 cm in length and width and within 5 – 20 cm in height. For the evaluation, some of boxes were sorted into three categories. The experiment result showed that the Automatic Sorting System is able to sort them out with an accuracy of 87.5%.

Contour-based object detection in Automatic Sorting System for a parcel boxes

Photovoltaic (PV) is a renewable electric energy generator that utilizes solar energy. PV is very suitable to be developed in Surabaya, Indonesia. Because Indonesia is located around the equator which has 2 seasons, namely the rainy season and the dry season. The dry season in Indonesia occurs in April to September. The power generated by PV is highly dependent on temperature and solar radiation. Therefore, accurate forecasting of short-term PV power is important for system reliability and large-scale PV development to overcome the power generated by intermittent PV. This paper proposes the Jordan recurrent neural network (JRNN) to predict short-term PV power based on temperature and solar radiation. JRNN is the development of artificial neural networks (ANN) that have feedback at each output of each layer. The samples of temperature and solar radiation were obtained from April until September in Surabaya. From the results of the training simulation, the mean square error (MSE) and mean absolute percentage error (MAPE) values were obtained at 1.3311 and 34.8820, respectively. The results of testing simulation, MSE and MAPE values were obtained at 0.9858 and 1.3311, with a time of 4.591204. The forecasting has minimized significant errors and short processing times.

Riky Tri Yunardi

Papers

Voice recognition system for controlling electrical appliances in smart hospital room

Holonomic Implementation of Three Wheels Omnidirectional Mobile Robot using DC Motors

An improved control for MPPT based on FL-PSo to minimize oscillation in photovoltaic system

Contour-based object detection in Automatic Sorting System for a parcel boxes

Short-term photovoltaics power forecasting using Jordan recurrent neural network in Surabaya