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This paper demonstrates the effectiveness of integrating computational intelligence to enhance the reliability of millimeter wave technology as a detection device for hazardous chemicals. The research explores the use of millimeter wave as an efficient and dependable alternative technology for chemical detection with the aid of machine learning to further improve its reliability and accuracy. This advancement is crucial in enabling security agencies, and authorities to remotely identify hazardous chemicals, minimizing risks to human lives and properties. The millimeter wave relies on natural non-ionizing radiation, which is of low power and considered safe for human exposure. The millimeter wave region used in this study is 77–81 GHz that offers short-pulse transmission capabilities, producing a wide spectrum of frequencies. These short pulses serve as the source for collecting the broadband spectral identity of chemicals, and the subsequent detection is post-processed with machine learning to increase the level of accuracy. The result of this study shows that by using computational intelligence models such as decision tree, k-nearest neighbor, support vector machine, and random forest, enhances the overall device reliability, and achieves higher detection accuracy based on the received reflected power. This result is comparable to an X-ray system device. [ABSTRACT FROM AUTHOR]

Extreme weather conditions such as heavy rainfalls have been wreaking havoc not only in urban areas but also in an entire watershed. The development of a flood management plan and flood mitigating structures to alleviate the impacts of flooding is very crucial because it needs intensive and continuous historical data. However, missing data due to equipment failure that gathers the rainfall data could be a problem. Rainfall data is not only useful in designing flood mitigating structures but also in planning our day-to-day activities ahead of time. To address this problem, this paper proposes a predictive model which able to forecast in a short lead-time and predict missing data within the dataset. In this paper, three predictive models will be compared namely recurrent neural network, Gaussian processing regression, and the proposed 6-gene genetic expression-based predictive modeling (MGGP). 29-year 24-hour cumulative rainfall data which were sourced in PAGASA Tacloban city weather station, Philippines, was used. The data were cleaned by removing negative values. Two datasets were created, the first (RFDS1) dataset which makes use of three indices (year, month, and days), and the second (RFDS2) dataset which was orchestrated and transformed to increase correlation and reduce prediction errors which had an additional two datasets (ave(t-1,t-2),t-1). Each method used three and five time-based indices. The result shows an erratic behavior of the model from three methods that used the RFDS1, while RFDS2 had a more stable predictive model. This shows that the data orchestration and transformation greatly improved the correlation and reduced errors. However, MGGP showed the best results among the three methods. [ABSTRACT FROM AUTHOR]

This study integrates fuzzy logic-controlled data switching and the radial basis function neural network (RBFNN) for fault detection and classification in grid-tied solar energy systems. The fuzzy logic controller filters out invalid sensor data through a data switch, ensuring that the fault detection and classification system receives reliable input. Training data were prepared through data normalization using the z-score function and principal component analysis, thereby reducing data complexity and standardizing the inputs. The resulting RBFNN model exhibited a low mean squared error with a value of 7.67×10-4, indicating its ability to classify faults based on the actual system scenarios. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess the effectiveness of the RBFNN model. The model demonstrated high accuracy (96.4%), precision (98.281%), recall (98.013%), and F1-score (98.147%), indicating the suitability and effectiveness of the RBFNN model to identify and classify faults in grid-tied solar energy systems. [ABSTRACT FROM AUTHOR]

Precise luffing of payload in mobile cranes is a crucial part of material handling and safety in industrial operations. Explosive ordnance disposal uses a bomb disposal robot to safely disable explosive devices at a safe distance. This robot is a mobile jib crane type with a gripper as an end effector instead of the typical suspension cable with a hook. The common challenge of this crane type is the arm/jib movement sensitivity with respect to tip-over stability of the crane body. This is directly influenced by the payload and constrained by the degree of freedom. This paper presents a control strategy of the joint for luffing such that the back wheels remain in contact with the surface ground and avoid bucking at any given instance of weight change in the payload. Fuzzy logic was applied to control the motor torque and luffing angle of the arm in response to the load and gripper opening size to maintain the tip-over stability margin at the highest value possible. The response curves at different configurations of the two input signals were also analyzed based on the rules set to determine its precision with respect to the expected response curve. [ABSTRACT FROM AUTHOR]

Constant demand for sustainable mechanisms to perform heavy and risky tasks has driven more robotics innovations to arise. Modular reconfigurable robotics system is one of these promising technologies that are continuously explored. Homogeneous types, to be specific, can accomplish similar missions at the same time as individual and heavier missions as an integrated system. This paper presents an analysis of the carrying capacity of a six-wheeled modular multi-agent system using the symbiotic model. The objective is to determine the resulting symbiotic relationship of a given configuration and module state combinations. The results show that the dominant relationship among the trials for linear traversal mission is commensalism. That means, the system neither benefits nor gets harmed from the symbiosis formed. This is true both in simulated and actual test environments although the percentage difference is about 12%. MATLAB Simulink was used for simulation while Maqueen robot in a 3D-printed chassis was used for actual testing. With this study, future configurations for several other missions such as object tracking and ramp climbing can be assessed using the same approach so that possible fault occurrence during operations can be prevented since the developed analysis method is performed prior to the deployment of the system. [ABSTRACT FROM AUTHOR]