With the rapid expansion of electric vehicles (EVs) and energy storage systems (ESS), ensuring the operational safety of lithium-ion batteries has become a critical technical challenge. Conventional battery management systems (BMS) primarily rely on threshold-based rule logic, which is limited in detecting coupled anomalies and early-stage degradation patterns. In this study, a deep learning-based framework for multivariate anomaly detection is proposed using BMS sensor data, including voltage, current, temperature, state of charge (SOC), and state of health (SOH). Five representative fault scenarios were defined, including thermal runaway precursors, cell voltage imbalance, SOC inconsistency, internal resistance increase, and communication delay. The proposed CNN-LSTM model was compared with conventional Rule-based methods and machine learning models, including Isolation Forest, Autoencoder, and LSTM. Experimental results show that the proposed model achieved the highest performance, with an F1-score of 0.885, an AUC of 0.94, and a detection delay of 8.1 s. In contrast, the Rule-based method exhibited a significantly higher false negative rate of 42.0%, indicating limitations in detecting complex anomaly patterns. These results demonstrate that the proposed spatiotemporal deep learning approach can significantly improve the accuracy and responsiveness of battery anomaly detection. Furthermore, the proposed method is expected to contribute to enhancing safety, reliability, and predictive diagnostics in next-generation intelligent BMS platforms.

This paper presents a comparative analysis of the fire detection characteristics between conventional fire detector sensors and an Si-based color sensor. With the rapid industrial development in modern society, the concentration of urban populations and the expansion of building sizes have accelerated, leading to an increased frequency of large-scale fires. As a result, the importance of fire detection technologies has been emphasized. However, conventional detectors continue to experience issues such as false alarms and malfunctions. To address these challenges, a novel fire detection technology utilizing an Si-based color sensor, which is effective for fire detection, is proposed. To evaluate the fire detection performance of each sensor, a fire detection test apparatus was developed, and experiments were conducted separately under smoke and flame conditions to analyze the fire detection capabilities of the Si-based color sensor, temperature sensor, and flame detection sensor. The experimental results demonstrated that detection speed and sensor values varied depending on the type of combustible material. Specifically, in the smoke and flame tests, the Si-based color sensor detected fires 26.7 and 43.7 seconds faster than the temperature sensor, and 26.6 and 15.4 seconds faster than the flame detection sensor, respectively. Therefore, it was confirmed that the Si-based color sensor proposed in this study is an effective detection technology that is expected to provide improved performance compared to conventional fire detectors.

Hazardous gas leakage incidents rank among the most serious safety accidents, leading to significant loss of life, extensive property damage, and severe environmental pollution. This paper describes an innovative IoT-based Assembly Double Pipe System (IADPS) designed for the prevention, early detection, and automated isolation of toxic gas leaks. The proposed system features a double-layered pipe design, with nitrogen charged between the inner and outer pipes, and gas detectors installed at strategic locations. This configuration is intended to prevent pipe corrosion, suppress ignition caused by escaping gas, and facilitate the early detection of gas leaks, thereby mitigating the risk of safety accidents. Furthermore, the system includes a comprehensive real-time monitoring system for pipe integrity and gas leakage, as well as an automated gas leakage detection and isolation system to quickly respond to any incidents.

This paper is an experimental study on the improvement of smoke and flame detection from different materials with YOLO. For the study, images of fires occurring in various materials were collected through an open dataset, and experiments were conducted by changing the main factors affecting the performance of the fire object detection model, such as the bounding box, polygon, and data augmentation of the collected image open dataset during data preprocessing. To evaluate the model performance, we calculated the values of precision, recall, F1Score, mAP, and FPS for each condition, and compared the performance of each model based on these values. We also analyzed the changes in model performance due to the data preprocessing method to derive the conditions that have the greatest impact on improving the performance of the fire object detection model. The experimental results showed that for the fire object detection model using the YOLOv5s6.0 model, data augmentation that can change the color of the flame, such as saturation, brightness, and exposure, is most effective in improving the performance of the fire object detection model. The real-time fire object detection model developed in this study can be applied to equipment such as existing CCTV, and it is believed that it can contribute to minimizing fire damage by enabling early detection of fires occurring in various materials.

This study proposes a crack identification algorithm to analyze the surface condition of porcelain insulators and to efficiently visualize cracks. The proposed image processing algorithm for crack identification consists of two primary steps. In the first step, the brightness is eliminated by converting the image to the lab color space. Then, the background is removed by the K-means clustering method. After that, the optimum image treatment is applied using morphological image processing and median filtering to remove unnecessary noise, such as blobs. In the second step, the preprocessed image is converted to grayscale, and any cracks present in the image are identified. Next, the region properties, such as the number of pixels and the ratio of the major to the minor axis, are used to separate the cracks from the noise. Using this image processing algorithm, the precision of crack identification for all the sample images was approximately 80%, and the F1 score was approximately 70. Thus, this method can be helpful for efficient crack monitoring.

This paper investigates the soundness of porcelain insulators associated with the acoustic emission (AE) technique. The AE technique is a popular non-destructive method that measures and analyzes the burst energy that occurs mainly when a crack occurs in a high-frequency region. Typical AE methods require continuous monitoring with frequent sensor calibration. However, in this study, the AE technique excites a porcelain insulator using only an impact hammer, and it applies a high-pass filter to the signal frequency range measured only in the AE sensor by comparing the AE and the acceleration sensors. Next, the extracted time-domain signal is analyzed for the damage assessment. In normal signals, the duration is about 2ms, the area of the envelope is about 1,000, and the number of counts is about 20. In the damage signal, the duration exceeds 5ms, the area of the envelope is about 2,000, and the number of counts exceeds 40. In addition, various characteristics in the time and frequency domain for normal and damage cases are analyzed using the short-time Fourier transform (STFT). Based on the results of the STFT analysis, the maximum energy of a normal specimen is less than 0.02, while in the case of the damage specimen, it exceeds 0.02. The extracted high-frequency components can present dynamic behavior of crack regions and eigenmodes of the isolated insulator parts, but the presence, size, and distribution of cracks can be predicted indirectly. In this regard, the characteristics of the surface crack region were derived in this study.

In this study, we produced a light, flexible, wearable gas sensor by depositing MWCNTs (Multi-walled Carbon Nanotubes) into nylon. MWCNTs are widely used as a gas sensor material due to their excellent mechanical, electrical and physical characteristics. We produced a gas sensor to detect NOx gases by depositing nylon yarn in a MWCNT solution. The MWCNT solution was made by mixing 3 mg MWCNT in 5 ml of ethanol. Nylon yarn was placed in the manufactured solution and ultrasonic waves were applied using an ultrasonicator for 3 h, resulting in MCWNT deposition. The MWCNT-deposited nylon yarn was dried at room temperature for 24 h. The MWCNT-thin-film-coated nylon yarn was masked 1 mm apart, and gold was then deposited on the masked nylon yarn to create the gas sensor. The sensor then was installed in a chamber with a controlled atmospheric environment and exposed to NOx gas. The changing signal from the sensor was amplified to analyze its gas detection characteristics.

The aim of this investigation is to detect specific waveforms in a distribution line prior to the occurrence of a fault. Conditions were introduced such that a feeder remote terminal unit (FRTU) of the distribution automation system selects and stores fault waveforms from the different waveforms detected in the distribution line. In addition, an algorithm was developed to detect specific waveforms from the fault waveforms stored using the FRTU. This algorithm exploits the duration and periodicity of harmonic changes in voltage and current. The efficacy of the algorithm was confirmed based on the measurements of fault waveforms in an actual distribution line. The results indicated that faults in a distribution line can be predicted via experimental measurements.

In particular, gas sensors require characteristics such as high speed, sensitivity, and selectivity. In this study, we fabricated a NO_X gas sensor by using a multi-walled carbon nanotube (MWCNT)/zinc oxide (ZnO) composite film. The fabricated MWCNT/ZnO gas sensor was then treated by a 450℃ temperature process to increase its detection sensitivity for NOx gas. We compared the detection characteristics of a ZnO film gas sensor, MWCNT film gas sensor, and the MWCNT/ZnO composited film gas sensor with and without the heat-treatment process. The fabricated gas sensors were used to detect NO_X gas at different concentrations. The gas sensor absorbed NO_X gas molecules, exhibiting increased sensitivity. The sensitivity of the gas sensor was increased by increasing the gas concentration. Additionally, while changing the temperature inside the chamber for the MWCNT/ZnO composite film gas sensor, we obtained its sensitivity for detecting NO_X gas. Compared with ZnO, the MWCNT film gas sensor is excellent for detecting NO_X gas. From the experimental results, we confirmed the enhanced gas sensor sensing mechanism. The increased effect by electronic interaction between the MWCNT and ZnO films contributes to the improved sensor performance.

Commonly, a live-line alarm can be used to measure the electric field strength of a high-voltage system to calculate its current, but it is hard to detect the electric field of shielded cables or concealed structures, such as underground distribution cables. Current sensors can detect the magnetic field in a single core wire, but they cannot determine the magnetic field about a double-core wire because the currents flow in opposite directions. Therefore, it is very difficult to detect certain current problems, such as a fault current in an extension line comprised of a double line. In this paper, to ultimately develop a sensor that can detect the current regardless of line conditions, we used a simulation to determine the concentration of the magnetic field dependent on the distribution of the external magnetic field and the path of each line’s core.

In this study, we fabricated an NOX gas sensor using a composite film of multi-walled carbon nanotubes (MWCNT)/zinc oxide (ZnO). Carbon nanotubes (CNTs) show good electronic conductivity and chemical-stability, and zinc oxide (ZnO) is a wide band gap semiconductor with a large exciton binding energy. Gas sensors require characteristics such as high speed, sensitivity, and selectivity. The fabricated gas sensor was used to detect NOX gas at different NOX concentrations. The sensitivity of the gas sensor increased with increasing gas concentrations. Additionally, while changing the temperature inside the chamber containing the MWCNT/ZnO gas sensor, we obtained the sensitivity and normalized responses for detecting NOX gas in comparison to ZnO and MWCNT film gas sensors. From the experimental results, we confirmed that the gas sensor sensing mechanism was enhanced in the composite-film gas-sensor and that the electronic interaction between MWCNT and ZnO contributed to the improved sensor performance.

This paper describes a low voltage detection circuit used in the semiconductor chips. The circuit was composed of a detection part of the CMOS structure as three stages and two inverters. The output of the low voltage detection circuit become to ‘high’ from ‘low’, when the power supply voltage falls below 80%. When the power supply voltage is 5 V, it was detected at 4 V point. The proposed low voltage detection circuit can be easily applied only by changing the resister and the capacitor without structural change in a wide range of power supply voltage.

In this paper, we fabricated organic compounds detector using the MWCNT/PMMA (multi-walled carbon nanotube / polymethylmethacrylate) composite film. We used polymer film as a matrix material for the device framework, and introduced CNTs for reacting with the organic compounds resulting in changing electrical conductivity. Spray coating method was used to form the MWCNT/PMMA composite film detector, and pattern formation of the detector was done by shadow mask during the spray coating process. We investigated changes of electrical conductivity of the detector before and after the organic compounds exposure. Electrical conductivity of the detector tended to decrease after the exposure with various organic compounds such as acetone, tetrahydrofuran(THF), toluene, and dimethylformamide (DMF). Finally we conclude that organic compounds detection by the MWCNT/PMMA composite film detector was possible, and expect the feasibility of commercial MWCNT/PMMA composite film detector for various organic compounds.

The humans are under attack involving the hazardous environment and pathogen/biotoxin aerosol that is realistic concerned. A portable, fast, reliable, and cheap Pathogen and Biotoxin Aerosol threat Detection(PBAD) trigger is an important technology for detect-to-protect and detect-to-treat system because the man-made biological terror is a fast and lethal infection. The ultraviolet C(UVC) wavelengths light source is key issue for PBAD that is sensitive because of strong fluorescence cross section from fluorescent amino acids in proteins such as tryptophan and tyrosine. The UVC-light emitting diode(LED) is emerging light source technology as alternative to laser or lamps as they offer several advantages. This paper discussed about the design consideration of UVC-LED for the PBAD system. The UVC-LED and PBAD technology, currently available or in development, are also discussed.

In this study, the temperature control device was designed for the study in order to detect theoutput in frequency of temperature, and the study confirmed accurate temperature values treatedsystemically by using expanded A/D converting Technology. The control technology of functional sensorincluded the output error Detection. For the future study, it is necessary to implement a control device bybuilding multiple circuits integrally with different types of sensors such as a automatically and intelligentnotification function sensors.

In a real-time indoor place recognition system using image features detection, specific markers included in input image should be detected exactly and quickly. However because the same markers in image are shown up differently depending to movement, direction and angle of camera, it is required a method to solve such problems. This paper proposes a technique to extract the features of object without regard to change of the object scale. To support real-time operation, it adopts SURF(Speeded up Robust Features) which enables fast feature detection. Another feature of this system is the user mark designation which makes possible for user to designate marks from input image for location detection in advance. Unlike to use hardware marks, the feature above has an advantage that the designated marks can be used without any manipulation to recognize location in input image.

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.