In Situ Surface Defect Detection in Polymer Tube Extrusion: AI-Based Real-Time Monitoring Approach.

While striving to optimize overall efficiency, smart manufacturing systems face various problems presented by the aging workforce in modern society. The proportion of aging workers is rapidly increasing worldwide, and visual perception, which plays a key role in quality control, is significantly susceptible to the impact of aging. Thus it is necessary to understand these changes and implement state-of-the-art technologies as solutions. In this study, we conduct research to mitigate the negative effects of aging on visual recognition through the synergistic effects of real-time monitoring technology combining cameras and AI in polymer tube production. Cameras positioned strategically and with sophisticated AI within the manufacturing environment promote real-time defect detection and identification, enabling an immediate response. An immediate response to defects minimizes facility downtime and enhances the productivity of manufacturing industries. With excellent detection performance (approximately 99.24%) and speed (approximately 20 ms), simultaneous defects in a tube can be accurately detected in real time. Finally, real-time monitoring technology with adaptive features and superior performance can mitigate the negative impact of decreased visual perception in aging workers and is expected to improve quality consistency and quality management efficiency.

Tool-Wear-Estimation System in Milling Using Multi-View CNN Based on Reflected Infrared Images.

A novel method for tool wear estimation in milling using infrared (IR) laser vision and a deep-learning algorithm is proposed and demonstrated. The measurement device employs an IR line laser to irradiate the tool focal point at angles of -7.5°, 0.0°, and +7.5° to the vertical plane, and three cameras are placed at 45° intervals around the tool to collect the reflected IR light at different locations. For the processing materials and methods, a dry processing method was applied to a 100 mm × 100 mm × 40 mm SDK-11 workpiece through end milling and downward cutting using a TH308 insert. This device uses the diffused light reflected off the surface of a rotating tool roughened by flank wear, and a polarization filter is considered. As the measured tool wear images exhibit a low dynamic range of exposure, high dynamic range (HDR) images are obtained using an exposure fusion method. Finally, tool wear is estimated from the images using a multi-view convolutional neural network. As shown in the results of the estimated tool wear, a mean absolute error (MAE) of prediction error calculated was to be 9.5~35.21 µm. The proposed method can improve machining efficiency by reducing the downtime for tool wear measurement and by increasing tool life utilization.

Temporal Estimation of Non-Rigid Dynamic Human Point Cloud Sequence Using 3D Skeleton-Based Deformation for Compression.

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured.

Phase-only hologram video compression using a deep neural network for up-scaling and restoration.

This paper proposes a coding method for compressing a phase-only hologram video (PoHV), which can be directly displayed in a commercial phase-only spatial light modulator. Recently, there has been active research to use a standard codec as an anchor to develop a new video coding for 3D data such as MPEG point cloud compression. The main merit of this approach is that if a new video codec is developed, the performance of relative coding methods can be increased simultaneously. Furthermore, compatibility is increased by the capability to use various anchor codecs, and the developing time is decreased. This paper uses a currently used video codec as an anchor codec and develops a coding method including progressive scaling and a deep neural network to overcome low temporal correlation between frames of a PoHV. Since it is difficult to temporally predict a correlation between frames of a PoHV, this paper adopts a scaling function and a neural network in the encoding and decoding process, not adding complexity to an anchor itself to predict temporal correlation. The proposed coding method shows an enhanced coding gain of an average of 22%, compared with an anchor in all coding conditions. When observing numerical and optical reconstructions, the result images by the proposed show clearer objects and less juddering than the result by the anchor.

3D Static Point Cloud Registration by Estimating Temporal Human Pose at Multiview.

This paper proposes a new technique for performing 3D static-point cloud registration after calibrating a multi-view RGB-D camera using a 3D (dimensional) joint set. Consistent feature points are required to calibrate a multi-view camera, and accurate feature points are necessary to obtain high-accuracy calibration results. In general, a special tool, such as a chessboard, is used to calibrate a multi-view camera. However, this paper uses joints on a human skeleton as feature points for calibrating a multi-view camera to perform calibration efficiently without special tools. We propose an RGB-D-based calibration algorithm that uses the joint coordinates of the 3D joint set obtained through pose estimation as feature points. Since human body information captured by the multi-view camera may be incomplete, a joint set predicted based on image information obtained through this may be incomplete. After efficiently integrating a plurality of incomplete joint sets into one joint set, multi-view cameras can be calibrated by using the combined joint set to obtain extrinsic matrices. To increase the accuracy of calibration, multiple joint sets are used for optimization through temporal iteration. We prove through experiments that it is possible to calibrate a multi-view camera using a large number of incomplete joint sets.

Dynamic Reconstruction and Mesh Compression of 4D Volumetric Model Using Correspondence-Based Deformation for Streaming Service.

A sequence of 3D models generated using volumetric capture has the advantage of retaining the characteristics of dynamic objects and scenes. However, in volumetric data, since 3D mesh and texture are synthesized for every frame, the mesh of every frame has a different shape, and the brightness and color quality of the texture is various. This paper proposes an algorithm to consistently create a mesh of 4D volumetric data using dynamic reconstruction. The proposed algorithm comprises remeshing, correspondence searching, and target frame reconstruction by key frame deformation. We make non-rigid deformation possible by applying the surface deformation method of the key frame. Finally, we propose a method of compressing the target frame using the target frame reconstructed using the key frame with error rates of up to 98.88% and at least 20.39% compared to previous studies. The experimental results show the proposed method's effectiveness by measuring the geometric error between the deformed key frame and the target frame. Further, by calculating the residual between two frames, the ratio of data transmitted is measured to show a compression performance of 18.48%.

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network.

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user's device to the cloud server without the captured original images. This paper proposes a new method to generate 3D (dimensional) keypoints based on a user's mobile device with a commercial RGB camera in a distributed computing environment such as a cloud server. The images are captured with a moving camera and 2D keypoints are extracted from them. After executing feature extraction between continuous frames, disparities are calculated between frames using the relationships between matched keypoints. The physical distance of the baseline is estimated by using the motion information of the camera, and the actual distance is calculated by using the calculated disparity and the estimated baseline. Finally, 3D keypoints are generated by adding the extracted 2D keypoints to the calculated distance. A keypoint-based scene change method is proposed as well. Due to the existing similarity between continuous frames captured from a camera, not all 3D keypoints are transferred and stored, only the new ones. Compared with the ground truth of the TUM dataset, the average error of the estimated 3D keypoints was measured as 5.98 mm, which shows that the proposed method has relatively good performance considering that it uses a commercial RGB camera on a mobile device. Furthermore, the transferred 3D keypoints were decreased to about 73.6%.

Rational Design and Synthesis of HSF1-PROTACs for Anticancer Drug Development.

PROTACs employ the proteosome-mediated proteolysis via E3 ligase and recruit the natural protein degradation machinery to selectively degrade the cancerous proteins. Herein, we have designed and synthesized heterobifunctional small molecules that consist of different linkers tethering KRIBB11, a HSF1 inhibitor, with pomalidomide, a commonly used E3 ligase ligand for anticancer drug development.

Selective targeting of cancer cells using a hydrogen peroxide-activated Hsp90 inhibitor.

Heat shock protein 90 (Hsp90) plays an important role in cancer cell proliferation, survival, and migration by regulating the maturation and stabilization of numerous oncoproteins. Despite significant efforts in developing Hsp90 inhibitors, none of these have been approved for clinical use, mostly due to toxicity, such as liver, cardiac, and retinal toxicity. To avoid undesirable toxicity, we herein report a hydrogen peroxide-activated Hsp90 inhibitor, Boro-BZide (3), which is capable of selectively targeting cancer cells over normal cells. Boro-BZide (3) can be activated by high levels of hydrogen peroxide, releasing its parent active Hsp90 inhibitor. The mechanism of action was determined by a series of experiments including fluorescence polarization assay, cell viability assay, western blotting, high-pressure liquid chromatography (HPLC), and fluorescence-activated cell sorting (FACS) analysis. These efforts ultimately led to the identification of a novel hydrogen peroxide-activated Hsp90 prodrug with improved therapeutic index, which was less prone to furnish unwanted adverse effects. This hydrogen peroxide-responsive prodrug strategy will be beneficial for overcoming the toxicity hurdles of Hsp90 inhibitors for clinical application.

Deep-learning-based hologram generation using a generative model.

We propose a new learning and inferring model that generates digital holograms using deep neural networks (DNNs). This DNN uses a generative adversarial network, trained to infer a complex two-dimensional fringe pattern from a single object point. The intensity and fringe patterns inferred for each object point were multiplied, and all the fringe patterns were accumulated to generate a perfect hologram. This method can achieve generality by recording holograms for two spaces (16 Space and 32 Space). The reconstruction results of both spaces proved to be almost the same as numerical computer-generated holograms by showing the performance at 44.56 and 35.11 dB, respectively. Through displaying the generated hologram in the optical equipment, we proved that the holograms generated by the proposed DNN can be optically reconstructed.

Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack.

This paper proposes a method to embed and extract a watermark on a digital hologram using a deep neural network. The entire algorithm for watermarking digital holograms consists of three sub-networks. For the robustness of watermarking, an attack simulation is inserted inside the deep neural network. By including attack simulation and holographic reconstruction in the network, the deep neural network for watermarking can simultaneously train invisibility and robustness. We propose a network training method using hologram and reconstruction. After training the proposed network, we analyze the robustness of each attack and perform re-training according to this result to propose a method to improve the robustness. We quantitatively evaluate the results of robustness against various attacks and show the reliability of the proposed technique.

Prediction of Drill Bit Breakage Using an Infrared Sensor.

In this paper, a novel drill bit breakage prediction method featuring a low-cost commercial infrared sensor to monitor drill bit corner wear is proposed. In the proposed method, the drill bit outer corner wear state can be monitored by measuring reflected infrared light because the reflection phenomenon is influenced by wear, edge shape, and surface roughness of the drill bit. In the experiments, a titanium workpiece was drilled without using cutting fluid to accelerate drill bit fracture. After drilling a hole in the workpiece, reflected infrared light was measured for the drill bit rotating at 100 rpm. Collected data on intensity of infrared light reflected from the circumferential surface of the drill bit versus the rotation angle of the drill bit were considered to predict tool breakage; two significant positions to predict tool breakage were found from the reflected infrared light graphs. By defining gradient vectors from the slopes of the reflected infrared light curves, a reliable criterion for determining drill bit breakage could be established. The proposed method offers possibilities for new measurement and analysis methods that have not been used in conventional tool wear and damage studies. The advantage of the proposed method is that the measurement device is easy to install and the measured signal is resistant to electromagnetic noise and ambient temperature because optical fiber is used as the signal transmission medium. It also eliminates the need for complex analysis of the measured signal, eliminating the need for a high-performance analyzer and reducing analysis time.

Holographic augmented reality based on three-dimensional volumetric imaging for a photorealistic scene.

In this paper, we propose a new system for a real-time holographic augmented reality (AR) video service based on a photorealistic three-dimensional (3D) object point for multiple users to use simultaneously at various locations and viewpoints. To observe the object from all viewpoints, a camera system capable of acquiring the 3D volume of a real object is developed and is used to generate a real object in real-time. Using the normal of the object point, the observable points are mapped to the viewpoint at which the user is located, and a hologram based on the object point is generated. The angle at which the reflected light from each point is incident on the hologram plane is calculated, and the intensity of the interference light is adjusted according to the angle to generate a hologram with a higher 3D effect. The generated hologram is transmitted to each user to provide a holographic AR service. The entire system consists of a camera system comprising eight RGB-D (depth) cameras and two workstations for photorealistic 3D volume and hologram generation. Using this technique, a realistic hologram was generated. Through experiments displaying holograms simultaneously from several different viewpoints, it is confirmed that multiple users can concurrently receive hologram AR.

New compression method for full-complex holograms using the modified zerotree algorithm with the adaptive discrete wavelet transform.

In this paper, we propose a new method for coding a full complex hologram with random phase. Since holograms with random phase have very unique spatial and frequency characteristics, a new compression method suitable for such holograms is required. We analyze the frequency characteristics of holograms with random phases and propose a new adaptive discrete wavelet transform (aDWT). Next, we propose a new modified zerotree alogrithm (mZTA) suitable for the subband configuration generated by the modified wavelet transform method. The results of the compression using the proposed method showed higher efficiency than the previous method, and the reconstructed images showed visually superior results.

Hybrid inhibitors of DNA and HDACs remarkably enhance cytotoxicity in leukaemia cells.

Chlorambucil is a nitrogen mustard-based DNA alkylating drug, which is widely used as a front-line treatment of chronic lymphocytic leukaemia (CLL). Despite its widespread application and success for the initial treatment of leukaemia, a majority of patients eventually develop acquired resistance to chlorambucil. In this regard, we have designed and synthesised a novel hybrid molecule, chloram-HDi that simultaneously impairs DNA and HDAC enzymes. Chloram-HDi efficiently inhibits the proliferation of HL-60 and U937 leukaemia cells with GI50 values of 1.24 µM and 1.75 µM, whereas chlorambucil exhibits GI50 values of 21.1 µM and 37.7 µM against HL-60 and U937 leukaemia cells, respectively. The mechanism behind its remarkably enhanced cytotoxicity is that chloram-HDi not only causes a significant DNA damage of leukaemia cells but also downregulates DNA repair protein, Rad52, resulting in the escalation of its DNA-damaging effect. Furthermore, chloram-HDi inhibits HDAC enzymes to induce the acetylation of α-tubulin and histone H3.

Structural Basis for Design of New Purine-Based Inhibitors Targeting the Hydrophobic Binding Pocket of Hsp90.

Inhibition of the molecular chaperone heat shock protein 90 (Hsp90) represents a promising approach for cancer treatment. BIIB021 is a highly potent Hsp90 inhibitor with remarkable anticancer activity; however, its clinical application is limited by lack of potency and response. In this study, we aimed to investigate the impact of replacing the hydrophobic moiety of BIIB021, 4-methoxy-3,5-dimethylpyridine, with various five-membered ring structures on the binding to Hsp90. A focused array of N7/N9-substituted purines, featuring aromatic and non-aromatic rings, was designed, considering the size of hydrophobic pocket B in Hsp90 to obtain insights into their binding modes within the ATP binding site of Hsp90 in terms of π-π stacking interactions in pocket B as well as outer α-helix 4 configurations. The target molecules were synthesized and evaluated for their Hsp90α inhibitory activity in cell-free assays. Among the tested compounds, the isoxazole derivatives 6b and 6c, and the sole six-membered derivative 14 showed favorable Hsp90α inhibitory activity, with IC50 values of 1.76 µM, 0.203 µM, and 1.00 µM, respectively. Furthermore, compound 14 elicited promising anticancer activity against MCF-7, SK-BR-3, and HCT116 cell lines. The X-ray structures of compounds 4b, 6b, 6c, 8, and 14 bound to the N-terminal domain of Hsp90 were determined in order to understand the obtained results and to acquire additional structural insights, which might enable further optimization of BIIB021.

Development of Thiazolidinedione-Based HDAC6 Inhibitors to Overcome Methamphetamine Addiction.

Thiazolidinedione is a five-membered heterocycle that is widely used in drug discovery endeavors. In this study, we report the design, synthesis, and biological evaluation of a series of thiazolidinedione-based HDAC6 inhibitors. In particular, compound 6b exerts an excellent inhibitory activity against HDAC6 with an IC50 value of 21 nM, displaying a good HDAC6 selectivity over HDAC1. Compound 6b dose-dependently induces the acetylation level of α-tubulin via inhibition of HDAC6 in human neuroblastoma SH-SY5Y cell line. Moreover, compound 6b efficiently reverses methamphetamine-induced morphology changes of SH-SY5Y cells via regulating acetylation landscape of α-tubulin. Collectively, compound 6b represents a novel HDAC6-isoform selective inhibitor and demonstrates promising therapeutic potential for the treatment of methamphetamine addiction.

Hair Metabolomics in Animal Studies and Clinical Settings.

Metabolomics is a powerful tool used to understand comprehensive changes in the metabolic response and to study the phenotype of an organism by instrumental analysis. It most commonly involves mass spectrometry followed by data mining and metabolite assignment. For the last few decades, hair has been used as a valuable analytical sample to investigate retrospective xenobiotic exposure as it provides a wider window of detection than other biological samples such as saliva, plasma, and urine. Hair contains functional metabolomes such as amino acids and lipids. Moreover, segmental analysis of hair based on its growth rate can provide information on metabolic changes over time. Therefore, it has great potential as a metabolomics sample to monitor chronic diseases, including drug addiction or abnormal conditions. In the current review, the latest applications of hair metabolomics in animal studies and clinical settings are highlighted. For this purpose, we review and discuss the characteristics of hair as a metabolomics sample, the analytical techniques employed in hair metabolomics and the consequence of hair metabolome alterations in recent studies. Through this, the value of hair as an alternative biological sample in metabolomics is highlighted.

Evaluation of the Inhibitory Effects of (E)-1-(2-hydroxy-4,6-dimethoxyphenyl)-3-(naphthalen-1-yl)prop-2-en-1-one (DiNap), a Natural Product Analog, on the Replication of Type 2 PRRSV In Vitro and In Vivo.

DiNap [(E)-1-(2-hydroxy-4,6-dimethoxyphenyl)-3-(naphthalen-1-yl)prop-2-en-1-one], an analog of a natural product (the chalcone flavokawain), was synthesized and characterized in this study. Porcine reproductive and respiratory syndrome virus (PRRSV) is the most challenging threat to the swine industry worldwide. Currently, commercially available vaccines are ineffective for controlling porcine reproductive and respiratory syndrome (PRRS) in pigs. Therefore, a pharmacological intervention may represent an alternative control measure for PRRSV infection. Hence, the present study evaluated the effects of DiNap on the replication of VR2332 (a prototype strain of type 2 PRRSV). Initially, in vitro antiviral assays against VR2332 were performed in MARC-145 cells and porcine alveolar macrophages (PAMs). Following this, a pilot study was conducted in a pig model to demonstrate the effects of DiNap following VR2332 infection. DiNap inhibited VR2332 replication in both cell lines in a dose-dependent manner, and viral growth was completely suppressed at concentrations ≥0.06 mM, without significant cytotoxicity. Consistent with these findings, in the pig study, DiNap also reduced viral loads in the serum and lungs and enhanced the weight gain of pigs following VR2332 infection, as indicated by comparison of the DiNap-treated groups to the untreated control (NC) group. In addition, DiNap-treated pigs had fewer gross and microscopic lesions in their lungs than NC pigs. Notably, virus transmission was also delayed by approximately 1 week in uninfected contact pigs within the same group after treatment with DiNap. Taken together, these results suggest that DiNap has potential anti-PRRSV activity and could be useful as a prophylactic or post-exposure treatment drug to control PRRSV infection in pigs.

High-speed computer-generated hologram based on resource optimization for block-based parallel processing: publisher's note.

This publisher's note corrects the author affiliations in Appl. Opt.57, 3511 (2018)APOPAI0003-693510.1364/AO.57.003511.