System In Various Environments Research Articles

Anomaly data synthesis and detection via domain randomization

The demand for a large amount of data necessary for learning is increasing with the great development of artificial intelligence. The synthesis of engineering data is challenging in that it is not only to combine data, but also to proceed with data synthesis while keeping the engineering characteristics intact. To address this problem, this work proposes a synthesis and detection model of anomalous data utilizing domain randomization. This model learns data from existing systems to identify patterns and synthesizes new data by itself with domain randomization. The learned model can accurately detect anomaly data in the system in various environments.

TransAAP: an automated annotation pipeline for membrane transporter prediction in bacterial genomes.

Membrane transporters are a large group of proteins that span cell membranes and contribute to critical cell processes, including delivery of essential nutrients, ejection of waste products, and assisting the cell in sensing environmental conditions. Obtaining an accurate and specific annotation of the transporter proteins encoded by a micro-organism can provide details of its likely nutritional preferences and environmental niche(s), and identify novel transporters that could be utilized in small molecule production in industrial biotechnology. The Transporter Automated Annotation Pipeline (TransAAP) (http://www.membranetransport.org/transportDB2/TransAAP_login.html) is a fully automated web service for the prediction and annotation of membrane transport proteins in an organism from its genome sequence, by using comparisons with both curated databases such as the TCDB (Transporter Classification Database) and TDB, as well as selected Pfams and TIGRFAMs of transporter families and other methodologies. TransAAP was used to annotate transporter genes in the prokaryotic genomes in the National Center for Biotechnology Information (NCBI) RefSeq; these are presented in the transporter database TransportDB (http://www.membranetransport.org) website, which has a suite of data visualization and analysis tools. Creation and maintenance of a bioinformatic database specific for transporters in all genomic datasets is essential for microbiology research groups and the general research/biotechnology community to obtain a detailed picture of membrane transporter systems in various environments, as well as comprehensive information on specific membrane transport proteins.

Comparative Study of Physics Engines for Robot Simulation with Mechanical Interaction

Simulation with a reasonable physical model is important to develop control algorithms for robots quickly, accurately, and safely without damaging the associated physical systems in various environments. However, it is difficult to choose the suitable tool for simulating a specific project. To help users in selecting the best tool when simulating a given project, we compare the performance of the four widely used physics engines, namely, ODE, Bullet, Vortex, and MoJoco, for various simple and complex industrial scenarios. We first summarize the technical algorithms implemented in each physics engine. We also designed four simulation scenarios ranging from simple scenarios for which analytic solution exists to complex industrial scenarios to compare the performance of each physics engine. We then present the simulation results in the default settings of all the physics engines, and analyze the behavior and contact force of the simulated objects.

Securing IoT Devices by Exploiting Backscatter Propagation Signatures

The low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks. Recent advances use an antenna array to extract fine-grained physical-layer signatures to identify the attackers, which adds burdens in terms of energy and hardware cost to IoT devices. In this paper, we present ShieldScatter, a lightweight system that attaches low-cost tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of tags. These signatures can be used to construct a sensitive profile to identify the location of the signals’ arrival, and thus detect the threat. In addition, we also design a tag-random scheme and a multiple receivers combination approach to detect a powerful attacker who has the strong priori knowledge of the legitimate user. We prototype ShieldScatter with USRPs and tags to evaluate our system in various environments. The results show that even when the powerful attacker is close to the legitimate device, ShieldScatter can mitigate 95 percent of attack attempts while triggering false alarms on just 7 percent of legitimate traffic.

Backscatter Wireless Communications and Sensing in Green Internet of Things

Internet of Things (IoT) is a key technology for enabling ubiquitous applications that interconnect with cyber-physical systems in various environments. However, its large scale adoption is strongly impeded by the limited energy available for most IoT devices that are battery-powered, and further challenged by the growing demands to pack increasing functionalities into IoT devices while shrinking their sizes. To address these problems, researchers have developed techniques for energy harvesting, wireless power transfer, and minimizing power consumption in the sensing, communication and computation components of IoT nodes, as found in many surveys. In contrast, this paper surveys Backscatter Communication (BackCom), a recently emerged technique that enables green IoT through joint wireless communication and sensing and potentially allows IoT devices to operate without batteries. The operating principle of BackCom-based green IoT, its architecture and evolution are presented. Also state-of-the-art applications such as healthcare, agriculture, human activity recognition, transportation and mobile IoT are reviewed together with the operational and security challenges faced by these applications and potential solution techniques to address these challenges while ensuring a high energy efficiency. Lastly, some future applications of BackCom-based green IoT are discussed.

Measurement and Evaluation of 28 GHz Propagation Characteristics in Specific Environments

New specific scenarios are expected for 5G systems in various environments, such as stadiums, subway platforms, factories, livestock barns. On the other hand, the applicability of conventional propagation models to new specific environments has not been clarified. In this paper, we report path loss and spatiotemporal characteristics such as delay spread (DS) and azimuth and zenith angular spread of arrival (ASA and ZSA) in the 28 GHz-band in various environments. By comparing the degree of fit to the conventional models, we considered whether the models for conventional scenarios assumed by previous mobile systems can be applied to the propagation characteristics in each specific environment. As a result, we clarify that the following points should be considered when applying the conventional model. The measured path loss characteristics were mostly close to those of the conventional models. However, the path loss in the non-line-of-sight with propagation to another room was larger than the conventional model, assuming there was a hall space environment with some blockers. Regarding the DS, we obtained the results that the characteristics were mainly affected by existence of long-distance delayed wave caused by a long narrow space and materials of the internal walls and ceiling. Larger ASA characteristics were obtained in the subway platform environment and the factory environment because of metallic obstacles and walls. Regarding the ZSA, a larger ZSA was obtained in the stadium due to scattering from spectator seats, and a smaller ZSA was obtained in the subway platform due to the low ceiling height and long propagation distance.

Bacteriophages of Thermophilic 'Bacillus Group' Bacteria-A Review.

Bacteriophages of thermophiles are of increasing interest owing to their important roles in many biogeochemical, ecological processes and in biotechnology applications, including emerging bionanotechnology. However, due to lack of in-depth investigation, they are underrepresented in the known prokaryotic virosphere. Therefore, there is a considerable potential for the discovery of novel bacteriophage-host systems in various environments: marine and terrestrial hot springs, compost piles, soil, industrial hot waters, among others. This review aims at providing a reference compendium of thermophages characterized thus far, which infect the species of thermophilic ‘Bacillus group’ bacteria, mostly from Geobacillus sp. We have listed 56 thermophages, out of which the majority belong to the Siphoviridae family, others belong to the Myoviridae and Podoviridae families and, apparently, a few belong to the Sphaerolipoviridae, Tectiviridae or Corticoviridae families. All of their genomes are composed of dsDNA, either linear, circular or circularly permuted. Fourteen genomes have been sequenced; their sizes vary greatly from 35,055 bp to an exceptionally large genome of 160,590 bp. We have also included our unpublished data on TP-84, which infects Geobacillus stearothermophilus (G. stearothermophilus). Since the TP-84 genome sequence shows essentially no similarity to any previously characterized bacteriophage, we have defined TP-84 as a new species in the newly proposed genus Tp84virus within the Siphoviridae family. The information summary presented here may be helpful in comparative deciphering of the molecular basis of the thermophages’ biology, biotechnology and in analyzing the environmental aspects of the thermophages’ effect on the thermophile community.

Five-mode quasilinear model of nonlinear dynamics of extended system

Distributed systems are widely used in practice. These are cosmic ligaments in the near-Earth space with a length of tens of kilometers. They approximate reinforced concrete piles in the soil when calculating the stress-strain state and assessing the technical condition; pipelines both in air and in liquid, underwater towed systems. Known underwater airlift systems of great length for the extraction of minerals (nodules) from the ocean floor with a length of 5-10 km. To solve the problems of the dynamics of such systems in various environments, the well-known mathematical models are not quite correct from the point of view of taking into account the variety of wave processes. It determines the need to build refined wave models. A new quasilinear mathematical model, which describes the nonlinear four-mode dynamics of the distributed system in the spatially inhomogeneous field of mass and surface forces, has been obtained. It is described by a nonlinear system of twelve first-order partial differential equations. For it, the principles of ultimate and hyperbolicity are fulfilled. Together with the boundary and initial conditions, it can be used to describe dynamics and statics of geometrically and physically nonlinear rod elements, piles in the ground, crane equipment ropes, mine lifts, aerial cableways, towed systems in liquid and gas flow, etc. For two-mode spatial reduction of the model, the theorem about correctness of Cauchy problem has been considered. As a result of the calculations, the earlier assumptions about the movement of the cable along its initial configuration were changed as the length of the cable changed. It has been found out that this assumption is only true for the initial transition participant. It is also established that at a given tachogram in the configuration of the towed line, there is a point of inflection, which shifts from top to bottom when lifting it. It can be a factor in the looping, contributing to the breakage of the cable system during towing.

Research on the adaptability of an improved high-intelligence long-distance optical fiber pre-warning system

With the development of technology, the total extent of global pipeline transportation is also increased each year. Nevertheless, owing to the wide distribution of gas and oil pipelines and the complex laying environment, the traditional long-distance optical fiber pre-warning system (OFPS) has a high false alarm rate when recognizing events threatening the pipeline safety, and it is difficult to define the type of intrusion events. Therefore, an improved high-intelligence long-distance OFPS was proposed, and the generalized adaptability of the system in various environments was studied. Φ-optical time-domain reflectometry technology was used in the distributed sensing part of the system, and a neural network (NN) was used in the signal recognition part to identify and classify intrusion events. Three methods were used in this system including an improved NN, a wavelet packet decomposition-based artificial NN, and a five-layer deep NN. Finally, through experiments in these three methods, the adaptability of the system was explored. The results show that the system possesses an excellent classification effect in the recognition of intrusion events with an average recognition rate reaching over 95%. Thus, it has good adaptability under various real environmental circumstances.

Scaling relations and substrate conditions controlling the complexity of Rhizophora prop root system

The complex structures of the prop root system of the mangrove genus Rhizophora attenuate storm surges and tsunamis and promote sedimentation. While quantification of the mangrove's drag force is essential for assessing the mangroves' ability to reduce the risk of disaster and vulnerability to rising sea-level, the projected area of prop root system having multiple orders of prop roots (e.g., first-, second-, and third-order prop roots, where the root order indicates the level of branching from the stem) are still unexplored. To contribute to the quantitative evaluation of the mangrove's drag force, we investigated the vertical profile of whole-tree prop root projected area and the number of prop roots of 156 trees sampled from Indonesia, the Philippines, and Japan. Our results showed that prop roots above the first-order contribute up to 80% of the whole-tree prop root projected area, highlighting the importance of the presence of second-, third-, and even higher-order prop roots on the drag force exerted by mangroves. Based on field data, an empirical model for the prop root system, described by a scaling factor (S), and maximum and minimum root heights (HRmax, HRmin), was developed by assuming that the size distribution of prop roots follows a constant scaling factor S. S and HRmax showed significant correlations with stem diameter at breast height suggesting the scaling relations in the prop root system. The model, which employs the site- and species-specific regression models for S and HRmax, reproduced the vertical distribution of the number of prop roots – a good predictor of the prop root projected area – with reasonable accuracy. However, the site- and species-specific scaling relations are highly variable depending on the environment, suggesting morphological plasticity in the prop root system. Rhizophora trees that grow on a shallow sediment layer (around 0.1 m thickness) tend to produce more prop roots compared with those on a thicker sediment layer suggesting a morphological response of the prop root system to increase below-ground root biomass where below-ground root development is physically constrained. Multivariate analysis indicated that hard substrate also possibly contributes to higher complexity of prop root system. However, prediction of the site- and species-specific scaling relations from these environmental variables still needs to be improved. Further research is needed to explain the mechanisms of morphological response of prop root systems to environmental gradients and to establish a generalized model for predicting the prop root system in various environments.

On the formation of Al2O3 nanofibers during self-propagating high-temperature synthesis of TiO2–Al–C system in various environments

The present study deals with the formation of Al2O3 fibers during self-propagating high-temperature synthesis of the TiO2–Al–C system triggered in argon, oxygen, nitrogen and air environments. Results showed TiC and Al2O3 were the primary products obtained in the argon and oxygen atmospheres. However, the dissolution of nitrogen in the structure of TiC resulted in the formation of Ti(C, N) after synthesis in nitrogen and air environments. Al2O3 fibers just were extensively attained in an air atmosphere with a low partial pressure of oxygen. Long nanofibers were primarily formed at the surface of the compact, while short fibers were developed at the inner part; especially adjacent to the internal pores. The formation mechanism of Al2O3 fibers was proposed as (a) the formation of Al2O gas through reaction of Al with O2 in the environment having low oxygen partial pressure like air; (b) further reaction of Al2O vapor with O2 and the development of Al2O3; and (c) precipitation of Al2O3 in the form of fibers by vapor-solid mechanism.

Exploring a Multimodal Mixture-Of-YOLOs Framework for Advanced Real-Time Object Detection

To construct a safe and sound autonomous driving system, object detection is essential, and research on fusion of sensors is being actively conducted to increase the detection rate of objects in a dynamic environment in which safety must be secured. Recently, considerable performance improvements in object detection have been achieved with the advent of the convolutional neural network (CNN) structure. In particular, the YOLO (You Only Look Once) architecture, which is suitable for real-time object detection by simultaneously predicting and classifying bounding boxes of objects, is receiving great attention. However, securing the robustness of object detection systems in various environments still remains a challenge. In this paper, we propose a weighted mean-based adaptive object detection strategy that enhances detection performance through convergence of individual object detection results based on an RGB camera and a LiDAR (Light Detection and Ranging) for autonomous driving. The proposed system utilizes the YOLO framework to perform object detection independently based on image data and point cloud data (PCD). Each detection result is united to reduce the number of objects not detected at the decision level by the weighted mean scheme. To evaluate the performance of the proposed object detection system, tests on vehicles and pedestrians were carried out using the KITTI Benchmark Suite. Test results demonstrated that the proposed strategy can achieve detection performance with a higher mean average precision (mAP) for targeted objects than an RGB camera and is also robust against external environmental changes.

Dynamic TDD Systems for 5G and Beyond: A Survey of Cross-Link Interference Mitigation

Dynamic time division duplex (D-TDD) dynamically allocates the transmission directions for traffic adaptation in each cell. D-TDD systems are receiving a lot of attention because they can reduce latency and increase spectrum utilization via flexible and dynamic duplex operation in 5G New Radio (NR). However, the advantages of the D-TDD system are difficult to fully utilize due to the cross-link interference (CLI) arising from the use of different transmission directions between adjacent cells. This paper is a survey of the research from academia and the standardization efforts being undertaken to solve this CLI problem and make the D-TDD system a reality. Specifically, we categorize and present the approaches to mitigating CLI according to operational principles. Furthermore, we present the signaling necessary to apply the CLI mitigation schemes. We also present information-theoretic performance analysis of D-TDD systems in various environments. As topics for future works, we discuss the research challenges and opportunities associated with the CLI mitigation schemes and signaling design in a variety of environments. This survey is recommended for those who are in the initial stage of studying D-TDD systems and those who wish to develop a more feasible D-TDD system as a baseline for reviewing the research flow and standardization trends surrounding D-TDD systems and to identify areas of focus for future works.

An online graph-based anomalous change detection strategy for unsupervised video surveillance

Due to various accidents and crime threats to an unspecified number of people, many surveillance technologies have been studied as an interest in individual security continues to increase throughout society. In particular, intelligent video surveillance technology is one of the most active research areas in the field of surveillance; this popularity has been spurred by recent advances in computer vision/image processing and machine learning. The main goal is to automatically detect, recognize, and analyze objects of interest from collected sensor information and then efficiently extract/utilize this useful information, such as by detecting abnormal events or intruders and recognizing objects. Anomalous event detection is a key component of security, and many existing anomaly detection algorithms rely on a foreground subtraction process to detect changes in the foreground scene. By comparing input image frames with a reference image, changed areas of the image can be efficiently detected. However, this technique can be insensitive to static changes and has difficulties in noisy environments since it depends on a reference image. We propose a new strategy for improved dynamic/static change detection that complements the weak points of existing detection methods, which have low robustness in noisy environments. To achieve this goal, we employed a self-organizing map (SOM) for data clustering and regarded the cluster distribution of neurons, represented by the weight of the optimized SOM, as a directed graph problem. We then applied the shortest path algorithm to recognize anomalous events. The real-time monitoring capability of the proposed change detection system was verified by applying it to self-produced test data and the CDnet-2014 dataset. This system showed robustness against noise that was superior to other surveillance systems in various environments.

CRNN-Based Multiple DoA Estimation Using Acoustic Intensity Features for Ambisonics Recordings

Localizing audio sources is challenging in real reverberant environments, especially when several sources are active. We propose to use a neural network built from stacked convolutional and recurrent layers in order to estimate the directions of arrival of multiple sources from a first-order Ambisonics recording. It returns the directions of arrival over a discrete grid of a known number of sources. We propose to use features derived from the acoustic intensity vector as inputs. We analyze the behavior of the neural network by means of a visualization technique called layerwise relevance propagation. This analysis highlights which parts of the input signal are relevant in a given situation. We also conduct experiments to evaluate the performance of our system in various environments, from simulated rooms to real recordings, with one or two speech sources. The results show that the proposed features significantly improve performances with respect to raw Ambisonics inputs.

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor.

Unmanned aerial vehicles (UAVs), which are commonly known as drones, have proved to be useful not only on the battlefields where manned flight is considered too risky or difficult, but also in everyday life purposes such as surveillance, monitoring, rescue, unmanned cargo, aerial video, and photography. More advanced drones make use of global positioning system (GPS) receivers during the navigation and control loop which allows for smart GPS features of drone navigation. However, there are problems if the drones operate in heterogeneous areas with no GPS signal, so it is important to perform research into the development of UAVs with autonomous navigation and landing guidance using computer vision. In this research, we determined how to safely land a drone in the absence of GPS signals using our remote maker-based tracking algorithm based on the visible light camera sensor. The proposed method uses a unique marker designed as a tracking target during landing procedures. Experimental results show that our method significantly outperforms state-of-the-art object trackers in terms of both accuracy and processing time, and we perform test on an embedded system in various environments.

All-Atom Continuous Constant pH Molecular Dynamics With Particle Mesh Ewald and Titratable Water.

Development of a pH stat to properly control solution pH in biomolecular simulations has been a long-standing goal in the community. Toward this goal recent years have witnessed the emergence of the so-called constant pH molecular dynamics methods. However, the accuracy and generality of these methods have been hampered by the use of implicit-solvent models or truncation-based electrostatic schemes. Here we report the implementation of the particle mesh Ewald (PME) scheme into the all-atom continuous constant pH molecular dynamics (CpHMD) method, enabling CpHMD to be performed with a standard MD engine at a fractional added computational cost. We demonstrate the performance using pH replica-exchange CpHMD simulations with titratable water for a stringent test set of proteins, HP36, BBL, HEWL, and SNase. With the sampling time of 10 ns per replica, most pKa's are converged, yielding the average absolute and root-mean-square deviations of 0.61 and 0.77, respectively, from experiment. Linear regression of the calculated vs experimental pKa shifts gives a correlation coefficient of 0.79, a slope of 1, and an intercept near 0. Analysis reveals inadequate sampling of structure relaxation accompanying a protonation-state switch as a major source of the remaining errors, which are reduced as simulation prolongs. These data suggest PME-based CpHMD can be used as a general tool for pH-controlled simulations of macromolecular systems in various environments, enabling atomic insights into pH-dependent phenomena involving not only soluble proteins but also transmembrane proteins, nucleic acids, surfactants, and polysaccharides.

디지털 무선 마을방송 시스템을 위한 서비스 커버리지 분석

Since, the existing analog village broadcasting system had been affected by the climate change such as rain and had some problems likes as short range, it had been changed to the digital system and its standardizations required to be developed from now. In order to deploy the efficient digital wireless village broadcasting system, some propagation profiles as well as service coverages should be analyzed in various radio environments. In this paper, we have set up the transmission power as a parameter and estimated the service coverage area ratio as well as the area ratio interfered into the nearby village in rural, suburban and urban environments using spectrum management intelligence system (SMIS). This paper provides the basic data to establish a standard using the radio propagation characteristics and received power for village broadcasting system in various environments.

Position Recognition System for Autonomous Vehicle Using the Symmetric Magnetic Field

The autonomous driving method using magnetic sensors recognizes the position by measuring magnetic fields in autonomous robots or vehicles after installing magnetic markers in a moving path. The Position estimate method using magnetic sensors has an advantage of being affected less by variation of driving environment such as oil, water and dust due to the use of magnetic field. It also has the advantages that we can use the magnet as an indicator and there is no consideration for power and communication environment. In this paper, we propose an efficient sensor system for an autonomous driving vehicle supplemented for existing disadvantage. In order to efficiently eliminate geomagnetism, we analyze the components of the horizontal and vertical magnetic field. We propose an algorithm for position estimation and geomagnetic elimination to ease analysis, and also propose an initialization method for sensor applied in the vehicle. We measured and analyzed the developed system in various environments, and we verify the advantages of proposed methods.

Airflow circulation cell study of an air-conditioning energy-saving mechanism

Abstract It is difficult for a traditional air-conditioning system to satisfy the thermal comfort demands of all occupants of a room as well as energy-saving goals. This is because, traditionally, air-conditioning airflow has been distributed without consideration of occupants’ needs beyond setting temperature and fan speed to meet thermal comfort standards, such as the predicted mean vote index. Therefore, this paper presents a regional air-conditioning mechanism (RACM) which not only can provide thermal comfort according to each occupant’s request, but may also significantly help save energy. The study investigated two areas: (1) the effect of cool air supply flow rate ( Q in ), air inlet temperature ( T in ), angle of inlet port ( φ 1 ), and angle of outlet port ( φ 2 ), and (2) determination of their suitable adjustment to create a better circulation cell. In the paper, nine simulated case studies were divided into three groups with various values of Q in , T in , φ 1 , and φ 2 . Group 2, in which T in and φ 2 were fixed for all case studies but values of Q in and φ 1 were not, was found to be the most sensitive in terms of vertical temperature profiles in the middle of the occupied zone in the room. It was also found that case 4 of group 2, in which Q in  = 43.09 m 3 /h, T in  = 23 °C, φ 1  = 85°, and φ 2  = 90°, consumed the least energy for the same level of thermal comfort demands compared with the two other groups. Two independent thermal environment regions were created in a simulated room, that is, the occupied zone and the rest of the room. Thus, the RACM can be successfully created. This investigation contributes to the research and design of RACM systems in various environments, including buses, trains, factories, public buildings, and so on.