Performance In Various Environments Research Articles

High Entropy Alloy Coatings and Technology

Recently, the materials research community has seen a great increase in the development of multicomponent alloys, known as high entropy alloys (HEAs) with extraordinary properties and applications. In surface protection and engineering, diverse applications of HEAs are also being counted to benefit from their attractive performances in various environments. Thermally sprayed HEA coatings have outperformed conventional coating materials and have accelerated further advancement in this field. Therefore, this review article overviews the initial developments and outcomes in the field of HEA coatings. The authors have also categorized these HEA coatings in metallic, ceramic, and composite HEA coatings and discussed various developments in each of the categories in detail. Various fabrication strategies, properties, and important applications of these HEAs are highlighted. Further, various issues and future possibilities in this area for coatings development are recommended.

Enabling 3D Ambient Light Positioning with Mobile Phones and Battery-Free Chips

Visible Light Positioning (VLP) has attracted much research effort recently. Most existing VLP approaches require special designed light or receiver, collecting light information or strict user operation (e.g., horizontally holding the mobile phone). This incurs a high deployment, maintenance and usage cost. We present RainbowLight, a low-cost ambient light 3D localization approach that is easy to deploy in today's buildings. Our key finding is that light through a chip of polarizer and birefringence material produces specific interference and light spectrum at different directions to the chip. We derive a model to characterize the relation for direction, light interference, and spectrum. Exploiting the model, RainbowLight calculates the direction to a chip after taking a photo containing the chip. With multiple chips, RainbowLight designs a direction intersection based method to derive the location. We implement RainbowLight and extensively evaluate its performance in various environments. The evaluation results show that RainbowLight achieves an average localization error of 3.3 cm in 2D and 9.6 cm in 3D for light on, and an error of 7.4 cm in 2D and 20.5 cm in 3D for light off scenario in the daytime.

CoHop

Cross-Technology Interference (CTI) badly harms the transmission reliability for low-power networks such as ZigBee at 2.4-GHz band. Though promising, channel hopping still faces challenges because the increasingly dense deployment of CTI leaves very few available channels. Selecting a good channel with the least overhead is crucial but challenging. Most of the existing works are heuristic methods that choose a channel far from the current one to avoid adjacent channels that may be correlatively interfered by CTI with a wider bandwidth such as WiFi. However, we observe that the correlated channels influenced by the same CTI source do not necessarily have the same channel qualities and even the opposite state, due to the uneven spectrum power density of CTI. Such channel opportunities are unexplored and wasted. In this article, we propose CoHop, a quantitative correlation-based channel hopping method for low-power wireless networks. We establish a quantitative model that describes the correlation of channel qualities to capture channel opportunities and calculate channel quality without probing, to reduce probing overhead. The probing sequence is optimized based on the Pearson Correlation Coefficient and the prediction-based probing algorithm. We implement CoHop on TinyOS and evaluate its performance in various environments. The experimental results show that CoHop can increase the Packet Reception Ratio by 80%, compared with existing methods.

Deep Reinforcement Learning: A State-of-the-Art Walkthrough

Deep Reinforcement Learning is a topic that has gained a lot of attention recently, due to the unprecedented achievements and remarkable performance of such algorithms in various benchmark tests and environmental setups. The power of such methods comes from the combination of an already established and strong field of Deep Learning, with the unique nature of Reinforcement Learning methods. It is, however, deemed necessary to provide a compact, accurate and comparable view of these methods and their results for the means of gaining valuable technical and practical insights. In this work we gather the essential methods related to Deep Reinforcement Learning, extracting common property structures for three complementary core categories: a) Model-Free, b) Model-Based and c) Modular algorithms. For each category, we present, analyze and compare state-of-the-art Deep Reinforcement Learning algorithms that achieve high performance in various environments and tackle challenging problems in complex and demanding tasks. In order to give a compact and practical overview of their differences, we present comprehensive comparison figures and tables, produced by reported performances of the algorithms under two popular simulation platforms: the Atari Learning Environment and the MuJoCo physics simulation platform. We discuss the key differences of the various kinds of algorithms, indicate their potential and limitations, as well as provide insights to researchers regarding future directions of the field.

REF: A Novel Forwarding Strategy in Wireless NDN

Named Data Networking (NDN), an emerging communication model in the content centric networks, has recently presented a solid framework for the future Internet. The NDN exploits content name instead of host name (IP address) and content caching. These features make NDN particularly efficient in networks with intermittent connectivity and dynamic topologies such as mobile ad hoc networks (MANETs). Due to the structure of NDN nodes, design of forwarding strategies has a vital impact on the network performance. In this paper, we propose a new forwarding strategy: redundancy elimination forwarding (REF). In REF, the NDN node structure and the operation of the data structures are modified. These modifications improve network performance in terms of throughput, overhead and resource requirements. REF strategy is simulated in different scenarios based on ndnSIM and the results demonstrate that it provides robust performance for various environments.

도심비행환경 3D GIS맵을 이용한 복합항법 성능 검증을 위한 Unity3D기반 PILS 설계

This paper presents PILS using Unity3D, a game development tool, for verification of navigation performance in various environments. The navigation performed with PILS is a fusion/complex navigation using GPS navigation and 3D GIS maps and multiple distance sensors in urban environments where satellite signals are not reliable. Accordingly, the topography and the building are constituted by utilizing the Unity3D program, and the virtual data are created by modeling sensors, vehicles, and vehicle’s guidance and controller. To create a more realistic simulation, actual sensor noise characteristics, and actual flight maneuvers are analyzed. Data are transferred to a navigation module used for actual flight by using serial communication, a navigation solution obtained from the navigation module is plotted in GCS. Solutions are transferred to a PC again where data are generated and finally compared with a true value in real-time.

Human Occupancy Detection via Passive Cognitive Radio.

Human occupancy detection (HOD) in an enclosed space, such as indoors or inside of a vehicle, via passive cognitive radio (CR) is a new and challenging research area. Part of the difficulty arises from the fact that a human subject cannot easily be detected due to spectrum variation. In this paper, we present an advanced HOD system that dynamically reconfigures a CR to collect passive radio frequency (RF) signals at different places of interest. Principal component analysis (PCA) and recursive feature elimination with logistic regression (RFE-LR) algorithms are applied to find the frequency bands sensitive to human occupancy when the baseline spectrum changes with locations. With the dynamically collected passive RF signals, four machine learning (ML) classifiers are applied to detect human occupancy, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), and linear SVM with stochastic gradient descent (SGD) training. The experimental results show that the proposed system can accurately detect human subjects—not only in residential rooms—but also in commercial vehicles, demonstrating that passive CR is a viable technique for HOD. More specifically, the RFE-LR with SGD achieves the best results with a limited number of frequency bands. The proposed adaptive spectrum sensing method has not only enabled robust detection performance in various environments, but also improved the efficiency of the CR system in terms of speed and power consumption.

COMPARATIVE PERFORMANCE ANALYSIS OF CHAOS BASED IMAGE ENCRYPTION TECHNIQUES

In the current scenario, the exchange of information over open network and internet have grown rapidly. Digital images constitute a major role in the multimedia communication. Hence their security becomes a major concern. The well-known solution for securing the data (in any form) is encryption. The traditional encryption techniques such as AES, DES, RSA, etc., exhibits a low level of security and significantly low level of resistance to the attacks. This problem can be overcome by chaotic image encryption techniques as they have a high level of randomness in key generation. This paper attempts to study various chaos-based image encryption algorithms and techniques and to compare their performance in various environments.

Integration of MoST and Graphit-iC coatings for the enhancement of tribological and corrosive properties

MoS2-based coatings present low friction coefficients in vacuum or inert environments, but they also present high wear rates in air environments, which is difficult to serve in complicate environments and extreme conditions. It is becoming more important to improve tribological properties in various environments for widely use. Conversely, carbon-based coatings exhibit good anti-wear properties in air environments, especially humid environments. In this study, we introduced the hydrogen-free carbon-chromium coating(Graphit-iC™) into the MoS2 coating doped with titanium(MoST™) by layer-by-layer deposition techniques, and we built a series of MoST/Graphit-iC multilayer coatings with different numbers of layers. The results show that multilayer coatings are composed of denser structure compared with the MoST coating. Furthermore, the tribological performance in various environments are significantly improved by building the multilayer structure. The lowest wear rate and longest wear life in ambient environment are obtained with 8 layers and the friction coefficient decreases to around 0.02 in vacuum environment. In addition, interfaces between MoST layers and Graphit-iC layers can inhibit the permeation of the corrosive mediums. Overall, the improvement of the tribological performance and corrosion resistance are attributed to the heterogeneous interfaces in multilayer coatings, and multilayer coatings exhibit great potential in widely use.

Enhancement of Iron Acquisition in Rice by the Mugineic Acid Synthase Gene With Ferric Iron Reductase Gene and OsIRO2 Confers Tolerance in Submerged and Nonsubmerged Calcareous Soils.

Iron (Fe) is an essential micronutrient for plants. Plants encounter Fe deficiency when grown in calcareous soil with low Fe availability, leading to reduced crop yield and agricultural problem. Rice acquires Fe from the soil via Strategy I–related system (ferrous ion uptake by OsIRT1) and Strategy II system (ferric ion uptake by chelation). However, rice plants have a weak ability in Fe(III) reduction and phytosiderophore secretion. We previously produced an Fe deficiency–tolerant rice harboring OsIRT1 promoter-refre1/372 (for higher Fe(III) reductase ability) and a 35S promoter-OsIRO2 (for higher phytosiderophore secretion). In this study, we produced a new Fe deficiency–tolerant rice by the additional introduction of a barley IDS3 genome fragment with refre1/372 and OsIRO2 (named as IRI lines) for further enhancement in Strategy II phytosiderophore productivity and better growth performance in various environments. Our results show that an enhanced tolerance was observed in OsIRO2 introduced line at the early growth stage, refre1/372 introduced line in the late stage, and RI line in all stages among five types of cultivation method. Moreover, we demonstrated that new IRI rice lines exhibited enhanced tolerance to Fe deficiency compared to nontransgenic (NT) rice and rice lines harboring the overexpressing OsIRO2 or the IDS3 fragment under submerged calcareous soil. The yields of IRI lines were ninefold higher than the NT line. Furthermore, under Fe-limited nonsubmerged calcareous soil condition (a new cultivation condition), IRI lines also conferred enhanced tolerance than NT, lines introducing only the OsIRT1 promoter-refre1/372 or overexpressing OsIRO2, and lines harboring both. Our results demonstrate that further enhancement of the Strategy II Fe uptake system by the mugineic acid synthase gene in addition to Fe uptake by enhanced ferric Fe reduction and phytosiderophore production in rice contributes Fe deficiency tolerance and broaden its utility in calcareous soil cultivation under paddy or nonpaddy field conditions.

Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-Valued Inverse Reinforcement Learning

In recent years, reinforcement learning (RL) methods have been applied to model gameplay with great success, achieving super-human performance in various environments, such as Atari, Go and Poker. However, these studies mostly focus on winning the game and have largely ignored the rich and complex human motivations, which are essential for understanding humans’ diverse behavior. In this paper, we present a multi-motivation behavior model which investigates the multifaceted human motivations and learns the underlying value structure of the agents. Our approach extends inverse RL to vectored-valued rewards with Pareto optimality which significantly weakens the inverse RL assumption. Our model therefore incorporates a wider range of behavior that commonly appears in real-world environments. For practical assessment, our algorithm is tested on World of Warcraft datasets and demonstrates the improvement over existing methods.

Analysis and Accurate Prediction of User’s Response Behavior in Incentive-Based Demand Response

Incentive-based demand response can fully mobilize a variety of demand-side resources to participate in the electricity market, but the uncertainty of user response behavior greatly limits the development of demand response services. This paper first constructed an implementation framework for incentive-based demand response and clarified how load-serving entity aggregates demand-side resources to participate in the power market business. Then, the characteristics of the user’s response behavior were analyzed; it is found that the user’s response behavior is variable, and it has a strong correlation on the timeline. Based on this, a prediction method of user response behavior based on long short-term memory (LSTM) is proposed after the analysis of the characteristics of the LSTM algorithm. The proposed prediction method was verified by simulation under the simulation environment setup by TensorFlow. The simulation results showed that, compared with the traditional linear or nonlinear regression methods, the proposed method can significantly improve the accuracy of the prediction. At the same time, it is verified by further experiments that the proposed algorithm has good performance in various environments and has strong robustness.

Hygroscopic Auxetic On-Skin Sensors for Easy-to-Handle Repeated Daily Use.

Despite the advance of on-skin sensors over the last decade, a sensor that solves simultaneously the critical issues for using in everyday life, such as stable performance in various environments, use over a long period of time, and repeated use by easy handling, has not yet been achieved. Here, we introduce an auxetic hygroscopic sensor that simultaneously meets all of the conditions. The auxetic structure with a negative Poisson's ratio matches with deformation of the skin in ankles; hence, a conformal contact between the sensor and the skin could be maintained during repeated movements. Sweat was absorbed in the auxetic electrode made of a hydrogel pattern coated with Ag nanowires and evaporated quickly; such hygroscopic characteristic led to excellent breathability. An electrocardiogram sensor and a haptic device were fabricated according to the proposed design for a sensor electrode. The sensors provide stable detecting performance in various environments, such as exercising, submersion in water, exposure to concentrated salt water, and continuous wearing for long time (7 days). Also, the sensors could be manually attached repeatedly without degrading the performance. This study provides new structural insights for on-skin sensors and presents future research directions.

Self-Diagnosis of Localization Status for Autonomous Mobile Robots.

It is essential to provide reliable localization results to allow mobile robots to navigate autonomously. Even though many state-of-the-art localization schemes have so far shown satisfactory performance in various environments, localization has still been difficult under specific conditions, such as extreme environmental changes. Since many robots cannot diagnose for themselves whether the localization results are reliable, there can be serious autonomous navigation problems. To solve this problem, this study proposes a self-diagnosis scheme for the localization status. In this study, two indicators are empirically defined for the self-diagnosis of localization status. Each indicator shows significant changes when there are difficulties in light detection and ranging (LiDAR) sensor-based localization. In addition, the classification model of localization status is trained through machine learning using the two indicators. A robot can diagnose the localization status itself using the proposed classification model. To verify the usefulness of the proposed method, we carried out localization experiments in real environments. The proposed classification model successfully detected situations where the localization accuracy is significantly degraded due to extreme environmental changes.

Route-Choice Modeling for Pedestrian Evacuation Based on Infrastructure Knowledge and Personal Preferences

In recent years, pedestrian simulation has been a valuable tool for the quantitative assessment of egress performance in various environments during emergency evacuation. For a high level of realism, an evacuation simulation requires a behavioral model that takes into account behavioral aspects of real pedestrians. In many studies, however, it is assumed that simulated pedestrians have a global knowledge of the infrastructure and choose either a predefined or the shortest route. It is questionable whether this simplification provides realistic results. This study addresses the problem of human-like route-choice behavior for microscopic pedestrian simulations. A route-choice model is presented that considers two concepts: first, the modeling of infrastructure knowledge to represent the variations in the decision-making processes of pedestrians with different degrees of familiarity with the infrastructure (e.g., regular commuters versus first-time visitors). Second, for each pedestrian the internal preference for selecting a certain path can be calibrated to allow the choice for the fastest routes or the ones that are most convenient for the agent (e.g., by avoiding stairs). The approach here uses a hybrid route-choice behavior model composed of a graph-based macrolevel representation of the environment, which is augmented with local information to avoid obstacles and dense crowds in the vicinity. This method was applied with different parameter sets in an evacuation study of a multilevel subway station. The results show the impact of these parameters on evacuation times, use of infrastructure elements, and crowd density at specific locations.

WiHumo: a real-time lightweight indoor human motion detection

WiFi is one of the most popular techniques, which has been used to detect human motion. In this paper, we extract channel state information (CSI) of wireless signal to detect human motion and prototype a detection system, WiHumo. First, we use a linear transformation to eliminate the shift of phases of different subcarriers. Subsequently, we design two criteria for the short-term case (SES) and the long-term case (LES), respectively. The former is to detect if someone is walking in the indoor room and the latter is to detect whether the person is walking continuously. We prototype the detection system with the commodity WiFi infrastructure and evaluate its performances in various environments. Experimental results show that WiHumo has high accuracy with real-time detection and outperforms the existing methods.

WiHumo: a real-time lightweight indoor human motion detection

WiFi is one of the most popular techniques, which has been used to detect human motion. In this paper, we extract channel state information (CSI) of wireless signal to detect human motion and prototype a detection system, WiHumo. First, we use a linear transformation to eliminate the shift of phases of different subcarriers. Subsequently, we design two criteria for the short-term case (SES) and the long-term case (LES), respectively. The former is to detect if someone is walking in the indoor room and the latter is to detect whether the person is walking continuously. We prototype the detection system with the commodity WiFi infrastructure and evaluate its performances in various environments. Experimental results show that WiHumo has high accuracy with real-time detection and outperforms the existing methods.

Locomotion Performance of the Amphibious Robot on Various Terrains and Underwater with Flexible Flipper Legs

Amphibious robots are attracting more and more attentions from researchers worldwide for their broad applications in resource exploration, disaster rescue, and reconnaissance. Amphibious robot with transformable flipper-leg composite propulsion mechanisms can adapt various terrestrial and water environments. In this paper, we explored the locomotion performance of a amphibious robot with flexible flipper legs on various terrains and underwater through dynamical simulation. The influence of the stiffness of the flipper legs on the locomotion performance in various environments was investigated comprehensively. The results indicate that the locomotion with flexible flipper legs is very stable, and the stiffness of the flipper legs has a great impact on the locomotion performance. The verification experiments demonstrate the accuracy of the simulation results. The study facilitates the design of the amphibious robot and indicates that the passively transformable flipper-leg mechanisms also enable amphibious robot to conquer various complex terrestrial environments.

Optimal frequency increment selection in frequency diverse multiple‐input–multiple‐output radar

Frequency diverse array (FDA) can provide a range-angle-dependent beam by controlling the frequency increment between adjacent elements. However, in conventional FDA, the frequency increment is fixed, resulting in a significant restrain of jamming performance in various environments. With the above finding, the authors propose an optimal frequency increment selection method by maximising the output signal-to-interference-plus-noise ratio in each coherent processing interval for multiple-input–multiple-output-FDA radar. In addition, the discrimination method for the target and the jamming is studied. Simulation results demonstrate that jamming can be effectively discriminated and suppressed with the radar configuration, and better performance is achieved with an optimal frequency increment.

COMPARISON OF EXPLORATION STRATEGIES FOR MULTI-ROBOT SEARCH

Searching for a stationary object in an unknown environment can be formulated as an iterative procedure consisting of map updating, selection of a next goal and navigation to this goal. It finishes when the object of interest is found. This formulation and a general search structure is similar to the related exploration problem. The only difference is in goal-selection, as search and exploration objectives are not the same. Although search is a key task in many search and rescue scenarios, the robotics community has paid little attention to the problem. There is no goal-selection strategy that has been designed specifically for search. In this paper, we study four state-of-the-art strategies for multi-robot exploration, and we evaluate their performance in various environments with respect to the expected time needed to find an object, i.e. to achieve the objective of the search.