Document image binarization is a challenging task because of combined degradation in a document. In this study, a new binarization method is proposed for binarizing an ancient document with combined degradation. The proposed method comprises the following four stages: histogram analysis, contrast enhancement, local adaptive thresholding, and artifact removal. In histogram analysis, a new approach is applied to establish a uniform background. Next, the image contrast is enhanced using a new contrast enhancement, and then the document is binarized using a novel local adaptive thresholding. Artifacts from the binarization process are removed in the artifact removal stage. Finally, an experiment is conducted using one private and four public datasets and by simulating the proposed method with and without contrast enhancement. The results showed that the proposed method is faster and more effective compared to other state-of-the-art procedures for binarizing ancient documents.

The need for electrical energy is increasing in line with the increase in population and increasing progress in welfare. On the other hand, the availability of fossil fuels as the main fuel in generating electricity is dwindling; so, there is a need for policies that require the use of environmentally friendly renewable energy. The utilization of renewable energy does not necessarily apply freely due to several constraints. One effort is a generator or distributed generation (DG) which is placed in the distribution line close to the load. The utilization of DG must go through planning, especially the large capacity and position on the bus and on the feeder, which will result in small network losses and a voltage profile that meets tolerance limits. Thus, the purpose of this study is to optimize to obtain the capacity and location of the DG calculated by considering the variation in the load through the genetic algorithm method. As a result, the optimal DG position for normal load is obtained on bus 18, bus 20, and bus 32 with capacities of 190 kW, 463 kW, and 370 kW, respectively. The losses obtained decreased from 54.6733 kW to 9.9447 kW, and the voltage profile was maintained within the specified limits. Optimization was carried out for decreasing and increasing loads in percent. The result is that losses can be minimized, and the voltage profile remains within the required limits. The lower the load, the more stable the voltage and the smaller the losses; meanwhile, the larger the load, the more fluctuating the voltage is, but still within the limits specified in the optimization.

In cross-spectral iris recognition, different spectral bands are used to obtain rich information of the human iris. Previous studies on cross-spectral iris recognition are based primarily on feature-based approaches, which are prone to the changes in parameters in the feature extraction process, such as spatial position and iris image acquisition conditions. These parameters can degrade iris recognition performance. In this paper, we present a phase-based approach for cross-spectral iris recognition using phase-only correlation (POC) and band-limited phase-only correlation (BLPOC). A phase-based iris recognition system recognizes an iris using the phase information contained in the iris image; therefore, its performance is not affected by feature extraction parameters. However, the performance of a phase-based cross-spectral iris recognition is strongly influenced by specular reflection. Different illumination conditions may produce different iris images from the same subject. To overcome this challenge, we integrate a photometric normalization technique -homomorphic filtering- with phase-based cross-spectral iris recognition. The experimental results reveal that the proposed technique achieved an excellent matching performance with an equal error rate of 0.59% and a genuine acceptance rate of 95%.

This paper proposes a new technique for a dual-axis light tracking system using a combination of Proportional Integral Derivative (PID) and Artificial Neural Network (ANN) based on a tetrahedron-based sensor. A tetrahedron-based sensor can detect the position of light using three photoresistor components arranged on a tetrahedron plane. The proposed technique combines PID and ANN controllers using a switching method that allows the algorithm to switch between PID and ANN based on the occurring errors. Additionally, in the proposed tracking system, a new mechanism called dual-axis in dual-frame is introduced. This mechanism involves the use of two frames moving in parallel along opposite axes. Experimental results collected the angular movement data and the decrease in error values. Numerical analysis showed that the proposed prototype of the PID-ANN tracking system demonstrates better response time than previous prototypes mentioned in the literature. Moreover, this system also exhibits low error rates and improved data distribution. Therefore, the PID-ANN tracking system with the switching process enhances the performance of responsive, accurate, and precise light tracking.

This paper examines the thermal condition aspects of traditional Acehnese houses (Rumoh Aceh) in Indonesia. This study highlights the importance of considering traditional architecture in achieving comfortable living conditions. Through the integration of cultural values, architectural design, and environmental factors, this research evaluates the prediction of thermal conditions within Rumoh Aceh based on building orientation and location, serving as guidelines for architects in designing buildings in the Aceh region. The study employs an ANN (Artificial Neural Network) algorithm to predict thermal condition parameters including indoor: temperature, humidity, and wind speed. The sample data used to train the ANN model consists of thermal data from five different rooms and meteorological data. The ANN model is employed to predict the thermal conditions in the Rumoh Aceh building based on its orientation and location. In this study, two models were developed, namely MODEL_01, for predicting indoor temperature and humidity, and MODEL_02, for predicting indoor wind speed. In MODEL_01, the error tolerance level obtained is ±1.05, and in MODEL_02, it is ±0.03. The prediction results based on the building orientation show the lowest average indoor temperature value of 29.19 °C, occurring at an angle of 292.19°. Furthermore, the lowest average indoor humidity value is 74.57 %, which also occurs at an angle of 292.19°. However, for indoor wind speed, the angle of 260° records the lowest average value of 0.125 m/s, while the highest average value is observed at 292.19°, at 0.132 m/s. The prediction results based on the building's location show the lowest average temperature value, which is 29.44 °C in the Aceh Besar region. Furthermore, the lowest average humidity value is 75.11 % in the North Aceh region. Meanwhile, the highest average wind speed value is 0.174 m/s in the Sabang region. This study reveals that at the orientation angle of 292.19° (the qibla direction), it produces optimal thermal conditions in terms of temperature and humidity with low wind speed. Other findings indicate that the Aceh Besar, North Aceh, and Sabang regions have optimal thermal conditions based on temperature, humidity, and wind speed.

This paper proposes an Artificial Neural Network (ANN) model using a Multi-Stage method to optimize the configuration of an External Lightning Protection System (ELPS) and grounding system. ELPS is a system designed to protect an area from damage caused by lightning strikes. Meanwhile, the grounding system functions to direct excess electric current from lightning strikes into the ground. This study identifies the optimal protection system configuration, reducing the need for excessive components. The ELPS configuration includes the number of protection pole units and the height of the protection poles. In contrast, the grounding system configuration consists of the number of electrode units and the length of the electrodes. This study focuses on the protection system configuration at a Photovoltaic Power Station, where the area is highly vulnerable to lightning strikes. Several aspects need to be considered in determining the appropriate configuration, such as average thunderstorm days per year, ELPS efficiency, total area of photovoltaic module, area to be protected, soil resistivity, electrode spacing factor, and the total required electrode resistance. The proposed multi-stage ANN model consists of three processing stages, each responsible for handling a portion of the overall system tasks. The first stage is responsible for determining the protection pole configuration. In the second stage, the Lightning Protection Level (LPL) classification is performed. Then, in the third stage, the process of determining the grounding configuration is handled. The analysis results show that the Multi-Stage ANN model can effectively determine the configuration with a low error rate: MAE of 0.265, RMSE of 0.314, and MPE of 9.533%. This model can also explain data variation well, as indicated by the high $R^2$ value of 0.961. The comparison results conducted with ATP/EMTP software show that the configuration produced by ANN results in fewer protection pole units but with greater height. Meanwhile, ANN produces a configuration with shorter electrode lengths but fewer units in the grounding system.