Anomaly Detection Based on Time Series Data of Hydraulic Accumulator.

Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data. Six anomaly detection algorithms were developed based on the acquired data. A threshold averaging algorithm over a period based on the averaged maximum/minimum thresholds detected anomalies 2.5 h before the hydraulic accumulator failure. In the support vector machine (SVM) and XGBoost model that distinguish normal and abnormal pulsating pressure data, the SVM model had an accuracy of 0.8571 on the test set and the XGBoost model had an accuracy of 0.8857. In a convolutional neural network (CNN) and CNN autoencoder model trained with normal and abnormal pulsating pressure images, the CNN model had an accuracy of 0.9714, and the CNN autoencoder model correctly detected the 8 abnormal images out of 11 abnormal images. The long short-term memory (LSTM) autoencoder model detected 36 abnormal data points in the test set.

Constrained Multiple Planar Reconstruction for Automatic Camera Calibration of Intelligent Vehicles.

In intelligent vehicles, extrinsic camera calibration is preferable to be conducted on a regular basis to deal with unpredictable mechanical changes or variations on weight load distribution. Specifically, high-precision extrinsic parameters between the camera coordinate and the world coordinate are essential to implement high-level functions in intelligent vehicles such as distance estimation and lane departure warning. However, conventional calibration methods, which solve a Perspective-n-Point problem, require laborious work to measure the positions of 3D points in the world coordinate. To reduce this inconvenience, this paper proposes an automatic camera calibration method based on 3D reconstruction. The main contribution of this paper is a novel reconstruction method to recover 3D points on planes perpendicular to the ground. The proposed method jointly optimizes reprojection errors of image features projected from multiple planar surfaces, and finally, it significantly reduces errors in camera extrinsic parameters. Experiments were conducted in synthetic simulation and real calibration environments to demonstrate the effectiveness of the proposed method.

Carbon Nanostructure of Diesel Soot Particles Emitted from 2 and 4 Stroke Marine Engines Burning Different Fuels.

Diesel soot particles were sampled from 2-stroke and 4-stroke engines that burned two different fuels (Bunker A and C, respectively), and the effects of the engine and fuel types on the structural characteristics of the soot particle were analyzed. The carbon nanostructures of the sampled particles were characterized using various techniques. The results showed that the soot sample collected from the 4-stroke engine, which burned Bunker C, has a higher degree of order of the carbon nanostructure than the sample collected from the 2-stroke engine, which burned Bunker A. Furthermore, the difference in the exhaust gas temperatures originating from the different engine and fuel types can affect the nanostructure of the soot emitted from marine diesel engines.

Probabilistic evaluation of surface-enhanced localized surface plasmon resonance biosensing.

In this paper, we investigate detection characteristics of localized surface plasmon resonance biosensing based on a probabilistic Poisson distribution of target molecules. The model uses random nanoislands for localization of near-fields in three detection scenarios of non-specific, non-colocalized, and colocalized detection. Optical signatures were found to increase monotonically with target concentration and size regardless of the detection scenarios. The signatures were largest in colocalized detection of target interactions to localized fields, followed by non-colocalized and non-specific detection. The confidence interval was the narrowest in the colocalized detection due to the increased spatial certainty by localization. Based on the relative confidence interval, it was found that limit of detection can be enhanced by more than four orders of magnitude through colocalization.

Surface plasmon-enhanced nanoscopy of intracellular cytoskeletal actin filaments using random nanodot arrays.

The feasibility of super-resolution microscopy has been investigated based on random localization of surface plasmon using blocked random nanodot arrays. The resolution is mainly determined by the size of localized fields in the range of 100-150 nm. The concept was validated by imaging FITC-conjugated phalloidin that binds to cellular actin filaments. The experimental results confirm improved resolution in reconstructed images. Effect of far-field registration on image reconstruction was also analyzed. Correlation between reconstructed images was maintained to be above 81% after registration. Nanodot arrays are synthesized by temperature-annealing without sophisticated lithography and thus can be mass-produced in an extremely large substrate. The results suggest a super-resolution imaging technique that can be accessible and available in large amounts.

Best basis selection method using learning weights for face recognition.

In the face recognition field, principal component analysis is essential to the reduction of the image dimension. In spite of frequent use of this analysis, it is commonly believed that the basis faces with large eigenvalues are chosen as the best subset in the nearest neighbor classifiers. We propose an alternative that can predict the classification error during the training steps and find the useful basis faces for the similarity metrics of the classical pattern algorithms. In addition, we also show the need for the eye-aligned dataset to have the pure face. The experiments using face images verify that our method reduces the negative effect on the misaligned face images and decreases the weights of the useful basis faces in order to improve the classification accuracy.

Unlocking the Power of Complement-Dependent Cytotoxicity: Engineering Strategies for the Development of Potent Therapeutic Antibodies for Cancer Treatments.

The complement system is a crucial part of the innate immune response, providing defense against invading pathogens and cancer cells. Recently, it has become evident that the complement system plays a significant role in anticancer activities, particularly through complement-dependent cytotoxicity (CDC), alongside antibody-dependent cell-mediated cytotoxicity (ADCC) and antibody-dependent cell-mediated phagocytosis (ADCP). With the discovery of new roles for serum complement molecules in the human immune system, various approaches are being pursued to develop CDC-enhanced antibody therapeutics. In this review, we focus on successful antibody engineering strategies for enhancing CDC, analyzing the lessons learned and the limitations of each approach. Furthermore, we outline potential pathways for the development of antibody therapeutics specifically aimed at enhancing CDC for superior therapeutic efficacy in the future.

Fatigue Analysis of a 40 ft LNG ISO Tank Container.

The demand for Liquefied natural gas (LNG) has rapidly increased over the past few years. This is because of increasingly stringent environmental regulations to curb harmful emissions from fossil fuels. LNG is one of the clean energy sources that has attracted a great deal of research. In the Republic of Korea, the use of LNG has been implemented in various sectors, including public transport buses, domestic applications, power generation, and in huge marine engines. Therefore, a proper, flexible, and safe transport system should be put in place to meet the high demand. In this work, finite element analysis (FEA) was performed on a domestically developed 40 ft ISO LNG tank using Ansys Mechanical software under low- and high-cycle conditions. The results showed that the fatigue damage factor for all the test cases was much lower than 1. The maximum principal stress generated in the 40 ft LNG ISO tank container did not exceed the yield strength of the calculated material (carbon steel). Maximum principal stress of 123.2 MPa and 107.61 MPa was obtained with low-cycle and high-cycle analysis, respectively, which is 50.28% less than the yield strength of carbon steel. The total number of cycles was greater than the total number of design cycles, and the 40 ft LNG ISO tank container was satisfied with a fatigue life of 20 years.

Optimal Hyperspectral Band Selection for Tissue Oxygenation Mapping with Generative Adversarial Network.

Tissue oxygenation assessment using hyperspectral imaging is an emerging technique for the diagnosis and pre- and post-treatment monitoring of ischemic patients. However, the high spectral resolution of hyperspectral imaging leads to large data sizes and a long imaging time. In this study, we propose a method that utilizes multi-objective evolutionary algorithms to determine the optimal hyperspectral band combination when developing a deep learning model for predicting tissue oxygenation from hyperspectral images. Our results confirm that the deep learning model effectively predicts tissue oxygenation images for various oxygenation states. Moreover, we demonstrate that a high-performance prediction model can be developed using only a small number of spectral bands, indicating the potential for more efficient non-contact tissue oxygenation mapping with the proposed method.Clinical Relevance- The proposed method allows for the non-contact and efficient acquisition of two-dimensional tissue oxygenation information in various oxygenation states.

Analysis and forecasting of national marine litter based on coastal data in South Korea from 2009 to 2021.

In this study, statistical analysis and forecasting were performed using coastal litter data of Korea. The analysis indicated that rope and vinyl accounted for the highest proportion of coastal litter items. The statistical analysis of the national coastal litter trends revealed that the greatest concentration of litter was observed during summer months (June-August). To predict the amount of coastal litter per meter, recurrent neural network (RNN)-based models were used. Neural basis expansion analysis for interpretable time series forecasting (N-BEATS) and neural hierarchical interpolation for time series forecasting (N-HiTS), an improved model of N-BEATS recently announced, were used for comparison with RNN-based models. When predictive performance and trend followability were evaluated, overall N-BEATS and N-HiTS outperformed RNN-based models. Furthermore, we found that average of N-BEATS and N-HiTS models yielded better results than using one model.

Nanoscale localization sampling based on nanoantenna arrays for super-resolution imaging of fluorescent monomers on sliding microtubules.

Sub-diffraction-limited imaging of fluorescent monomers on sliding microtubules in vitro by nanoscale localization sampling (NLS) is reported. NLS is based on periodic nanohole antenna arrays that create locally amplified electromagnetic hot spots through surface plasmon localization. The localized near-field hot spot temporally samples microtubular movement for enhanced spatial resolution. A fourfold improvement in spatial resolution compared to conventional wide-field microscopy is demonstrated. The resolution enhancement is achieved by imaging rhodamine-labeled microtubules that are sampled by the hot spots to provide sub-diffraction-limited images at 76 nm resolution in the direction of movement and 135 nm orthogonally. The intensity distribution produced by the NLS is measured to be broader than that of conventional imaging, which is consistent with the improvement of imaging resolution. Correlation studies between neighboring nanoantennas are also performed. This confirms the possibility of measuring microtubular transport dynamics. NLS can be useful for moving objects that have a high labeling density or for performing fluctuation spectroscopy in small volumes, and may allow "super-resolution on demand" by customizing nanoantenna structures for specific resolution needs.

Field-matter integral overlap to estimate the sensitivity of surface plasmon resonance biosensors.

We have analyzed the effectiveness of field-matter integral overlap between target index distribution and local near-fields to assess detection sensitivity of surface plasmon resonance (SPR) biosensors. The correlation of the overlap with sensitivity was clear. An overlap integral defined with lateral electric field intensity produced the highest correlation due to tangential continuity across a boundary. Among the three detection scenarios considered, the correlation for localized SPR sensing was slightly lower than that of thin film-based detection and improved with an increased fill factor in the structure. The results will be useful to maximize the optical signature created by target interactions and to produce highest sensitivity of SPR detection to variations when target or field distribution is not uniform.

Intelligent emergency stop algorithm for a manipulator using a new regression method.

In working environments with large manipulators, accidental collisions can cause severe personal injuries and can seriously damage manipulators, necessitating the development of an emergency stop algorithm to prevent such occurrences. In this paper, we propose an emergency stop system for the efficient and safe operation of a manipulator by applying an intelligent emergency stop algorithm. Our proposed intelligent algorithm considers the direction of motion of the manipulator. In addition, using a new regression method, the algorithm includes a decision step that determines whether a detected object is a collision-causing obstacle or a part of the manipulator. We apply our emergency stop system to a two-link manipulator and assess the performance of our intelligent emergency stop algorithm as compared with other models.

Colocalization of gold nanoparticle-conjugated DNA hybridization for enhanced surface plasmon detection using nanograting antennas.

We have investigated enhanced surface plasmon resonance detection through colocalization of gold nanoparticle (GNP)-conjugated target molecules and near-fields established by nanograting-based antennas. The target colocalization was implemented by angled dielectric thin-film deposition on the nanograting structure. The concept was tested by detecting DNA hybridization and shows that the colocalization produces an additional 60%-80% increase of resonance shifts. The colocalization involves a much smaller number of target molecules, so that the measured enhancement per molecule by the colocalization of GNP-conjugated DNA oligomers was estimated to be by more than 2 orders of magnitude relative to that of thin-film-based conventional detection.

Plasmonics-based spatially activated light microscopy for super-resolution imaging of molecular fluorescence.

In this Letter, we explore plasmonics-based spatially activated light microscopy (PSALM) for sub-diffraction-limited imaging of biomolecules. PSALM is based on the spatially switched activation of local amplified electromagnetic hot spots under multiple light incidence conditions. The hot spots are associated with surface plasmons that are excited and localized by surface nanostructures. The feasibility of the concept was demonstrated by imaging fluorescent nanobeads on a two-dimensional gold nanograting of a 100-nm-wide grating ridge, the size of which is the measure of the imaging resolution. The result confirms the performance of PSALM for imaging nanobeads at a resolution below the conventional diffraction limit.

Application of soot discharged from the combustion of marine gas oil as an anode material for lithium ion batteries.

Many studies have recently investigated the characteristics of combustion products emitted from ships and onshore plant facilities for use as energy sources. Most combustion products that have been reported until now are from heavy oils, however, no studies on those from light oils have been published. This study attempted to use the combustion products from the light oils from naval ships as anode materials for lithium ion batteries (LIBs). These products have a carbon black morphology and were transformed into highly crystalline carbon structures through a simple heat treatment. These new structured materials showed reversible capacities of 544, 538, 510, 485, 451 and 395 mA h g-1 at C-rates of 0.1, 0.2, 0.5, 1.0, 2.0 and 5.0C, respectively, and excellent rate performance. These findings were the result of a combination hierarchical pores ranging from the meso- to macroscale and the high capacitive charge storage behavior of the soot. The results of this study prove that annealed soot with a unique multilayer graphite structure shows promising electrochemical performance suitable for the production of low-cost, high-performance LIB anode materials.

Correction: Application of soot discharged from the combustion of marine gas oil as an anode material for lithium ion batteries.

[This corrects the article DOI: 10.1039/D0RA07195A.].

Conversion of Black Carbon Emitted from Diesel-Powered Merchant Ships to Novel Conductive Carbon Black as Anodic Material for Lithium Ion Batteries.

Waste soot generated from diesel engine of merchant ships has ≥ 2 µm agglomerates consisting of 30-50 nm spherical particles, whose morphology is identical to that of carbon black (CB) used in many industrial applications. In this study, we crystallized waste soot by heat treatment to transform it into a unique completely graphitic nano-onion structure, which is considerably different from that of commercial conductive CB. While commercial CB has a large specific surface area because of many surface micropores generated due to quenching by water-spraying in the production process, the heat-treated waste soot has a smooth micropore-free surface. Thus, the treated waste soot acquires the shape of CB but has a much smaller specific surface area. When the treated soot is used as a conductive material in lithium ion battery (LIB) half cells, the Coulombic efficiency of the entire anode is improved significantly owing to its low specific surface area; the electrochemical performance of the LIB is considerably enhanced compared to that of conventional conductive materials. Thus, polluting soot generated in marine propulsion can be transformed into a new class of CB with a unique structure by simple heat treatment; this soot can also be used as an inexpensive conductive material to enhance the LIB performance.

Recycling Waste Soot from Merchant Ships to Produce Anode Materials for Rechargeable Lithium-Ion Batteries.

In this study, the waste soot generated by ships was recycled to produce an active material for use in lithium-ion batteries (LIBs). Soot collected from a ship was graphitized by a heat treatment process and used as an anode active material. It was confirmed that the graphitized soot was converted into a highly crystalline graphite, and was found to form carbon nano-onions with an average diameter of 70 nm. The graphitized soot showed a high discharge capacity and an excellent cycle life, with a reversible capacity of 260 mAhg-1 even after 150 cycles at a rate of 1 C. This study demonstrates that the annealed soot with a unique graphitic multilayer structure has an electrochemical performance that renders it suitable as a candidate for the production of low-cost anode materials for use in LIBs.

Ultra-Sensitive Surface Plasmon Resonance Detection by Colocalized 3D Plasmonic Nanogap Arrays.

Ultra-sensitive detection based on surface plasmon resonance (SPR) was investigated using 3D nanogap arrays for colocalization of target molecular distribution and localized plasmon wave in the near-field. Colocalization was performed by oblique deposition of a dielectric mask layer to create nanogap at the side of circular and triangular nanoaperture, where fields localized by surface plasmon localization coincide with the spatial distribution of target molecular interactions. The feasibility of ultra-sensitivity was experimentally verified by measuring DNA hybridization. Triangular nanopattern provided an optimum to achieve highly amplified angular shifts and led to enhanced detection sensitivity on the order of 1 fg/mm2 in terms of molecular binding capacity. We confirmed improvement of SPR sensitivity by three orders of magnitude, compared with conventional SPR sensors, using 3D plasmonic nanogap arrays.