An Improved Measurement Method for the Strength of Radiation of Reflective Beam in an Industrial Optical Sensor Based on Laser Displacement Meter

An optical sensor such as a laser range finder (LRF) or laser displacement meter (LDM) uses reflected and returned laser beam from a target. The optical sensor has been mainly used to measure the distance between a launch position and the target. However, optical sensor based LRF and LDM have numerous and various errors such as statistical errors, drift errors, cyclic errors, alignment errors and slope errors. Among these errors, an alignment error that contains measurement error for the strength of radiation of returned laser beam from the target is the most serious error in industrial optical sensors. It is caused by the dependence of the measurement offset upon the strength of radiation of returned beam incident upon the focusing lens from the target. In this paper, in order to solve these problems, we propose a novel method for the measurement of the output of direct current (DC) voltage that is proportional to the strength of radiation of returned laser beam in the received avalanche photo diode (APD) circuit. We implemented a measuring circuit that is able to provide an exact measurement of reflected laser beam. By using the proposed method, we can measure the intensity or strength of radiation of laser beam in real time and with a high degree of precision

Robust Localization for Robot and IoT Using RSSI

Node-localization technology has been supported in the wireless sensor network (WSN) environment. Node localization is based on a few access-point (AP) nodes that comprises positioning information because they are fixed, and a beacon node that comprises unknown positioning information because it is moving. To determine the position of the unknown node, it must use two or three APs that comprise certain positioning information. There are a number of representative range-based methods, including time of arrival (TOA), weighted centroid locating algorithm, received signal strength intensity (RSSI), and time difference of arrival (TDOA) signal, that are received by the receiver. The RSSI method has its advantages. A simple device structure means that the RSSI method is easy to use. Because the structures of previous wireless local area network (LAN) technologies make them compatible with RSSI information, the RSSI method is widely used in the related area of position tracking. In addition, this algorithm has a hardware system that cannot be increased, has the advantage of the miniaturization of the node, and can wear through obstacles. This paper proposes the application of a robust ranging method that can be applied in robots and Internet of Things (IoT) using RSSI, especially in the tracing location of each nursing home patient, where the RSSI method with trilateral technique is used. This paper shows the results of the measured point from the application of the trilateral technique, and it also represents the results of the error distance between the ideal point and the measured point using computer simulation. Finally, this paper presents an estimation of localization using a real experimental device with a BLE (Bluetooth low-energy) transmitter and receiver, and beacon gateway, by applying an RSSI algorithm with the trilateral technique

An Improved Measurement Method for the Strength of Radiation of Reflective Beam in an Industrial Optical Sensor Based on Laser Displacement Meter

An optical sensor such as a laser range finder (LRF) or laser displacement meter (LDM) uses reflected and returned laser beam from a target. The optical sensor has been mainly used to measure the distance between a launch position and the target. However, optical sensor based LRF and LDM have numerous and various errors such as statistical errors, drift errors, cyclic errors, alignment errors and slope errors. Among these errors, an alignment error that contains measurement error for the strength of radiation of returned laser beam from the target is the most serious error in industrial optical sensors. It is caused by the dependence of the measurement offset upon the strength of radiation of returned beam incident upon the focusing lens from the target. In this paper, in order to solve these problems, we propose a novel method for the measurement of the output of direct current (DC) voltage that is proportional to the strength of radiation of returned laser beam in the received avalanche photo diode (APD) circuit. We implemented a measuring circuit that is able to provide an exact measurement of reflected laser beam. By using the proposed method, we can measure the intensity or strength of radiation of laser beam in real time and with a high degree of precision

Prospective Research Trend Analysis on Zero-Energy Building (ZEB): An Artificial Intelligence Approach

While global attention to zero-energy building (ZEB) has surged as a sustainable countermeasure to high-energy consumption, a congruent expansion in research remains conspicuously absent. Addressing this lacuna, our study harnesses public research and development grant data to decipher evolving trajectories within ZEB research. Distinctively departing from conventional methodologies, we employ state-of-the-art natural language processing (NLP) artificial intelligence models to meticulously analyze grant textual content pertinent to ZEB. Our findings illuminate an expansive spectrum of ZEB-related research, with a pronounced focus on the holistic continuum of energy supply, demand, distribution, and actualization within architectural confines. Theoretically, this work delineates key avenues ripe for future empirical exploration, fostering a robust academic foundation for subsequent ZEB inquiries. Practically, the insights derived bear significant implications for practitioners, informing optimal implementation strategies, and offering policymakers coherent roadmaps for sustainable urban development. Collectively, this study affords a panoramic perspective on contemporary ZEB research contours, enhancing both scholarly comprehension and practical enactment in this pivotal domain

Predicting the Amount of Electric Power Transaction Using Deep Learning Methods

The most important thing to operate a power system is that the power supply should be close to the power demand. In order to predict the amount of electric power transaction (EPT), it is important to choose and decide the variable and its starting date. In this paper, variables that could be acquired one the starting day of prediction were chosen. This paper designated date, temperature and special day as variables to predict the amount of EPT of the Korea Electric Power company. This paper also used temperature data from a year ago to predict the next year. To do this, we proposed single deep learning algorithms and hybrid deep learning algorithms. The former included multi-layer perceptron (MLP), convolution neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine regression (SVR), and adaptive network-based fuzzy inference system (ANFIS). The latter included LSTM + CNN and CNN + LSTM. We then confirmed the improvement of accuracy for prediction using pre-processed variables compared to original variables We also assigned two years of test data during 2017–2018 as variable data to measure high prediction accuracy. We then selected a high-accuracy algorithm after measuring root mean square error (RMSE) and mean absolute percent error (MAPE). Finally, we predicted the amount of EPT in 2018 and then measured the error for each proposed algorithm. With these acquired error data, we obtained a model for predicting the amount of EPT with a high accuracy

Analysis of Chaotic Behavior in a Novel Extended Love Model Considering Positive and Negative External Environment

The aim of this study was to describe a novel extended dynamical love model with the external environments of the love story of Romeo and Juliet. We used the sinusoidal function as external environments as it could represent the positive and negative characteristics of humans. We considered positive and negative advice from a third person. First, we applied the same amount of positive and negative advice. Second, the amount of positive advice was greater than that of negative advice. Third, the amount of positive advice was smaller than that of negative advice in an external environment. To verify the chaotic phenomena in the proposed extended dynamic love affair with external environments, we used time series, phase portraits, power spectrum, Poincare map, bifurcation diagram, and the maximal Lyapunov exponent. With a variation of parameter “a”, we recognized that the novel extended dynamic love affairs with different three situations of external environments had chaotic behaviors. We showed 1, 2, 4 periodic motion, Rössler type attractor, and chaotic attractor when parameter “a” varied under the following conditions: the amount of positive advice = the amount of negative advice, the amount of positive advice > the amount of negative advice, and the amount of positive advice < the amount of negative advice

Chaotic Dynamics of the Fractional-Love Model with an External Environment

Based on the fractional order of nonlinear system for love model with a periodic function as an external environment, we analyze the characteristics of the chaotic dynamic. We analyze the relationship between the chaotic dynamic of the fractional order love model with an external environment and the value of fractional order (α, β) when the parameters are fixed. Meanwhile, we also study the relationship between the chaotic dynamic of the fractional order love model with an external environment and the parameters (a, b, c, d) when the fractional order of the system is fixed. When the parameters of fractional order love model are fixed, the fractional order (α, β) of fractional order love model system exhibit segmented chaotic states with the different fractional orders of the system. When the fractional order (α = β) of the system is fixed, the system shows the periodic state and the chaotic state as the parameter is changing as a result

Sine Rotation Vector Method for Attitude Estimation of an Underwater Robot

This paper describes a method for estimating the attitude of an underwater robot. The method employs a new concept of sine rotation vector and uses both an attitude heading and reference system (AHRS) and a Doppler velocity log (DVL) for the purpose of measurement. First, the acceleration and magnetic-field measurements are transformed into sine rotation vectors and combined. The combined sine rotation vector is then transformed into the differences between the Euler angles of the measured attitude and the predicted attitude; the differences are used to correct the predicted attitude. The method was evaluated according to field-test data and simulation data and compared to existing methods that calculate angular differences directly without a preceding sine rotation vector transformation. The comparison verifies that the proposed method improves the attitude estimation performance