Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection and Classification of Anomalies in Internet of Vehicles using Convolutional Neural Networks

Internet of Vehicles in brief is the distributed networks that makes the use of data produced due to connected cars and vehicular ad-hoc networks. With the progress in Science and Technology, The Internet of Vehicles has got an exponential boost, leading to tremendous progress in the field. The smart vehicles are equipped with various sensors which are data collection points, from which various useful data is collected for the processing and operating of the intelligent vehicles and play a vital role in implementation and communication of the vehicles. The smart vehicles has been a boon to humans which include fewer accidents, faster travel times, less theft due to tracking. But with these advantages, the vehicles have to cope up with various malicious intrusions and issues that compromise with the security and integrity of the vehicle. This is one of the major issues in the IOV which makes the data more vulnerable to be exposed. The research work aims to make the system more secure and safe by the detection and classification of the attacks that make these smart vehicles unsafe. The approach used in this work is based on the CNN based architecture VGG19, in which the data obtained from OBD port of vehicle and converted to visual format is feed. The types of the attacks included in this research work for the classification are Denial of Service Attack (DoS), Spoofing(RPM Spoof, Gear Spoof), Fuzzy Attack. The purpose of the research is to design a solution that makes the system more secure, preserves the privacy and increase the trustability of the user.
Vehicle Trajectory Prediction in Roundabout Based on the Joint Learning of Taillight State and Historical Trajectory

In complex urban road environments, especially at junctions such as roundabout and crossroads, predicting the future behaviour of surrounding vehicles can provide advanced awareness of possible hazards, thus helping decision-making and planning modules to act more efficiently and safely. Therefore, trajectory prediction is of great significance to the autonomous vehicles. However, trajectory prediction faces the problem of ‘unclear intention', ‘uncertain right-of-way' and ‘frequent vehicle interaction' in interactive scenarios like roundabout. Therefore, this paper proposes a taillight state and historical trajectory joint learning method based on multi-head attention (MHA [15]) and LSTM [17] for vehicle trajectory prediction in roundabout. Specifically, the historical trajectory and taillight state sequence are encoded by MHA and LSTM respectively and concatenated together. On the condition of the current trajectory of ego vehicle, the feature information is input into a decoder LSTM network to predict the future trajectory of two target vehicles. Our method is trained and tested in the Carla [16] simulation environment and gets considerable results. Considering the error of taillight recognition in practice, the model is also tested under different taillight recognition accuracy and proven to be robust and practical. The dataset is published on: https://pan.baidu. com/s/1DWaWNbdUpj6UjYQNicqFDg? pwd=49jy.
Toward Cloud Resolving Modeling of Large-Scale Tropical Circulations: A Simple Cloud Microphysics Parameterization

Abstract This paper discusses cloud microphysical processes essential for the large-scale tropical circulations and the tropical climate, as well as the strategy to include them in large-scale models that resolve cloud dynamics. The emphasis is on the ice microphysics, which traditional cloud models consider in a fairly complex manner and where a simplified approach is desirable. An extension of the classical warm rain bulk parameterization is presented. The proposed scheme retains simplicity of the warm rain parameterization (e.g., only two classes of condensed water are considered) but introduces two important modifications for temperatures well below freezing:1) the saturation conditions are prescribed based on saturation with respect to ice, not water; and 2) growth characteristics and terminal velocities of precipitation particles are representative for ice particles, not raindrops. Numerical tests suggest that, despite its simplicity, the parameterization is able to capture essential aspects of the .
Average Spectral Efficiency for TDD-based non-Stationary XL-MIMO with VR Estimation

Extra large scale massive multiple-input multiple-output (XL-MIMO) has become a key technology for the next generation wireless communications. This paper considers a multi-user XL-MIMO system in non-stationary scenarios, where the impact of spatial non-stationarity is characterized by visibility region (VR). We first estimate VR of users by using the received training signal with the pilot sequences since VR information is unknown. Then, we estimate the channel from users to the BS antennas within their corresponding VR estimation. By using the VR and channel estimations, we derive an approximation of the average spectral efficiency and design the optimal VR detection threshold to maximize the average spectral efficiency. Finally, we confirm the correctness of our analysis and the effectiveness of the proposed method by simulation and numerical analysis.
LuckyAgent2022: A Stop-Learning Multi-Armed Bandit Automated Negotiating Agent

The 13th automated negotiation competition was held in 2 leagues (ANL2022 and SCML2022) in conjunction with the 31st IJCAI conference. The ANL for 2022 is bilateral negotiation under the SOAP protocol. Agents are allowed to learn from their previous negotiations. The agents could have 3 main BOA components: a Bidding strategy that decides which bid and when must be sent to the opponent, an Opponent model that tries to model the opponent's preferences, and an Acceptance strategy that decides whether to accept the opponent's offer or not. This paper explains our LuckyAgent2022's BOA components and its learning methods over negotiation sessions. To improve its utility over sessions, we propose SLM, a LSN Stop-Learning mechanism, to prevent overfitting by adapting it to a multi-armed bandit problem. It finds the best value for variables of a time-dependent bidding strategy for the opponent.
UAV Landing Control Disturbed by Carrier Air-Wake Based on Deep Reinforcement Learning

A method based on deep reinforcement learning (DRL) is proposed to solve the autonomous landing control problem of unmanned aerial vehicle (UAV) under the interference of carrier air-wake. Firstly, a simulation model of UAV landing on a carrier is built, and a reference trajectory to ensure its safe landing is designed. Secondly, a UAV autonomous landing control method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is proposed, and the reward function is designed that takes into account tracking error and trajectory stability. Finally, through offline training, an autonomous landing controller is obtained. The simulation results show that the controller designed in this paper can not only accurately control the landing trajectory of UAVs, but also cope with complex effects of nonlinear air-wake in nonlinear dynamic inverse (NDI) control, and greatly improve the safety of landing.
Second-Order Reconfigurable Half-Mode Substrate Integrated Waveguide Dual-Band Filter: a Circuit Perspective

In this work, a tunable dual-band filter has been designed. A triangular Half-Mode Substrate Integrated Waveguide (HM-SIW) resonators have been used to miniaturize the filter to get a smaller device footprint. A dual-band filter operating at 9 GHz and 14 GHz first and second pass-band center frequency is built using Rogers-5880 substrate with a permittivity of 2.2. To reconfigure the center frequencies of the first and second pass-bands, dielectric rods have been used to perturb the structure. The diameter of the dielectric rods are varied from 0.2 mm to 0.8 mm, and the center frequency for the first pass-band changed from 8.1 GHz to 9.15 GHz, while the center frequency of the second pass-band changed from 14.61 GHz to 16.10 GHz. The dielectric rods' permittivity was also changed to alter the structure's overall permittivity and a frequency change from 8.1 GHz to 9.1 GHz, and 14.61 GHz to 14.74 GHz has been observed for the first and second pass-band, respectively.
Operational Road Map in North Sulawesi and Gorontalo Power Grid Considering the Intermittent Photovoltaic Penetration

In 2019, the North Sulawesi and Gorontalo power system welcomed the gradual entry of photovoltaics into the high voltage grid. The first phase of the solar power plant in North Sulawesi was Likupang PV Plant at 15 MWp and Isimu PV plant at 10 MWp. The North Sulawesi and Gorontalo system is characterized by a daytime peak load of 330 MW. This way, the initial penetration of photovoltaics into the system at this stage is 7.58%. However, the system's nadir stiffness is only within a range of 10–16 MW/Hz. Therefore, the expected frequency fluctuations from intermittent photovoltaics could reach higher than 1 Hz, so it is necessary to re-engineer the operation of the electric power system. This paper presents several stages that PLN as the grid operator needs to develop, starting from the readiness of the SCADA system, adjusting the defense scheme, improving the operation of the system, and PV forecasting. Implementing the photovoltaic curtailment can be an alternative when there is a very high intermittence which causes vulnerability to the system during operation. Developments in the operation scheme and technology for photovoltaics are still carried out in stages, such as using photovoltaics curtails as a reserve margin to energy storage technologies such as the Battery Energy Storage System (BESS). The aim of the roadmap is to increase system reliability and maximize Photovoltaic utilization
Review of Smart Street Lighting Research in Indonesia

Street lighting is a necessity in open spaces especially roadside. The longer the road in a city, the number of street lighting needed become much more and it makes the government spends much more money to pay the electricity bills. One of the efforts usually used to reduce the electricity cost is by changing the commonly used street lamp to a more energy-efficient lamp such as LED (Light Emitting Diode). In the cities which have moved towards to the smart cities concept such as Jakarta in Indonesia, the technology used for street lighting is smart street lighting (SSL). The street lighting is equipped with a system consisting of sensors, communication devices, and energy management to make the street lighting become automatically adaptable to the environment. It reduces the electricity bill more effective than just only changing the lamp to LED. The use of SSL in Indonesia cannot be separated from the research which had been done and is still being developed about it. The research about smart street lighting in Indonesia had been conducted by some researchers with generally taking the topic about lighting controller and monitor development and the effectiveness comparison between the use of SSL and general street lighting. This paper reviews SSL research which had been carried out by researchers in Indonesia.
Scale effect on flow and thermal boundaries in micro‐/nano‐channel flow using molecular dynamics–continuum hybrid simulation method

The molecular dynamics (MD)–continuum hybrid simulation method has been developed in two aspects in the present work: (1) The energy equation has been combined into the coupling method in order to obtain the hybrid temperature profile and (2) the coupling method has been improved by the local linearization to obtain a smoother parametric profile. The developed method is primarily validated by analytical solutions and full MD results. Then, it is employed to study the scale effect on the flow and thermal boundaries in micro‐/nano‐channel flow. The hybrid velocity and temperature profiles are obtained with the channel height (H) ranging from 60σ to 2014σ and the solid–liquid coupling (β) ranging from 0.1 to 50. Scale effect has shown strong influence on the boundaries. Obvious slip characteristics can be found in the profiles, i.e. velocity slip and temperature jump, when H is small and β is large. However, the results also show that the profiles can be well predicted to converge to the macroscale non‐slip/non‐jump analytical solutions when H is large enough, where the effect of β can be omitted and the slip characteristics disappear. Correlations of relative slip length, relative temperature jump and pressure gradient with H are fitted from the simulation results. Copyright © 2009 John Wiley & Sons, Ltd.
Non-contact Monitoring Of Ambulatory Blood Pressure Based On Webcam

Measurements of ambulatory blood pressure can provide a vital physiological assessment. However, attempts so far are contact and non-automated, susceptible to external factors and typically expensive. In this paper, a non-contact method of measuring ambulatory blood pressure is presented. This approach allows for efficient pulse rate acquisition directly from face image captured from webcam and is based on imaging PhotoPlethysmoGraphic. In essence, given the two pulse signals, the phase difference and the heart rate can be obtained. The desired signal was acquired by the functional relationship between phase difference, heart rate, and blood pressure. The proposed algorithm is thoroughly assessed on a dataset containing 249 videos. Using Bland-Altman for correlation analysis, we compared blood pressure extracted from video recorded by a basic webcam with the sphygmomanometer and achieved high accuracy and correlation. Furthermore, the proposed technique can measure more than one person at a time and may have a great value in monitoring a person at home.
A Review on Acne Detection Techniques

Acne has become a common problem in the daily lives of most suffering from skin disorders. It can be classified into three types such as inflammatory, non-inflammatory, and a mixture of both inflammatory and non-inflammatory lesions. Studies show that it can persist in a majority of women younger than 30 and a good number of women under 50. Women contribute two-thirds of the total visits made to a dermatologist throughout the year. Acne has been the most common skin disorder. Hence, it becomes extremely important to design a system that can accurately detect and classify the acne regions on one's face. In this paper, we will be taking stock of various acne detection methods involving traditional and improved algorithms on various datasets. We will also be reviewing some of the image preprocessing techniques to separate and extract the acne lesion more efficiently.
A fast initial alignment method for strapdown inertial navigation system on stationary base

In this paper a strapdown inertial navigation system (SINS) error model is introduced, and the observability of the SINS error model is analyzed. Then, on the basis of this SINS error model and the analysis of computer simulation results, a fast estimation method of the azimuth error /spl Phi//sub D/ is proposed for the initial alignment of SINS on stationary base. Consequently, the time of initial alignment is reduced greatly. The computer simulation results illustrate the efficiency of this fast initial alignment method.
Distributed assembly scheduling problem with three stages and maintenance: a new artificial bee colony

In this study, distributed assembly scheduling problem (DASP) with three stages named fabrication, transportation and assembly, and maintenance is considered. A new artificial bee colony with Q-learning (QABC) is proposed to minimize maximum tardiness. An effective Q-learning algorithm is implemented to dynamically select search operator, which consists of 12 states based on population quality evaluation, 8 actions defined by global search and neighborhood search, a new reward and an effective action selection. Two employed bee swarms are formed, an adaptive communication and an adaptive competition process between them are adopted. QABC and its four comparative algorithms are tested on 80 instances. The computational results demonstrate that the new strategies of QABC really improve its search performance and QABC is a competitive algorithm for the considered problem.
Identification of Accident Path Elements using Supervised Learning: Observation of Diminishing Marginal Accuracy while using Cosine Similarity

With increasing outsourcing of project activities to contractors in process industries, ensuring contractor safety is becoming one of the top priorities of organizations. This study aims at identifying the major Hazardous Elements (HE) and Initiating Events (IE) of incidents occurring at a steel plant in India with respect to activities and safety incidents involving contractors. Accordingly, a supervised text classification model is used. First a training dataset is manually annotated to identify the HEs and IEs and then HE and IE are annotated for a test document based on cosine similarity values calculated between the test document and the training documents. Accordingly, 11 unique HEs and IEs have been identified in contractor safety dataset. This study also attempts to identify the optimal way to use cosine similarity to classify a test dataset. First, for each test document, top N training documents in decreasing order of cosine similarity values are considered for classification and the HE and IE as displayed by majority of those training documents are taken as the HE and IE of the test document. The percentage of accurately classified test documents is then calculated. In the next step, the study monitors the change in accuracy percentage as N is stepwise increased. The results show that on increasing N, the accuracy increases but the marginal increase starts diminishing after a threshold.
A common theoretical framework for visual motion's spatial and temporal coherence

A recently proposed computational theory for the perception of spatially coherent visual motion (see A.L. Yuille et al., 1988) is extended to include temporal coherence . Particularly, it is argued that a good extension is to postulate that the direction of visual motion does not change much in time. The extended theory is consistent with the psychophysical phenomena of motion capture, motion cooperativity, nonrigid wave motion, and motion inertia. The authors also discuss the possible roles of coherence for motion perception. It is shown that spatial coherence improves the signal-to-noise ratio of the perceived motion, solves the aperture problem, and simplifies the correspondence problem. Temporal coherence also simplifies the correspondence problem and speeds up its solution. <<ETX>>
A Deep Reinforcement Learning Approach for Multi-UAV-Assisted Data Collection in Wireless Powered IoT networks

Unmanned aerial vehicle (UAV) has been widely deployed in efficient data collection for Internet of Things (IoT). UAV can not only act as a relay, but also as an energy source to provide information and energy transmission for ground sensor nodes (SNs). This paper studies the efficient multi-UAV-assisted data collection problem in wireless powered IoT. Specifically multiple UAVs wirelessly charge SNs using radio frequency (RF) energy transfer, and the SNs then use the harvested energy to upload the updates of the sensed information to the UAVs, thus improving the freshness of collected data and extending the service time of the SNs. The problem is modeled as a partially observed Markov decision process (POMDP) with a large observation and action space, where each UAV acts as an intelligent agent to learn the environment and make decisions independently. The value-decomposition network (VDN) algorithm is employed to find the optimal strategy in the multi-agent deep reinforcement learning framework. Simulation results validate the effectiveness of the proposed data collection approach compared to two baseline policies.
Physical Layer Authentication Based on DOA and Rotational State

This paper studies the physical layer security for the downlink of cellular networks. Specifically, a user equipment (UE) should identify whether the received signals is from a legal base station (BS) or a rogue BS. Toward this end, a new physical layer authentication (PLA) scheme is proposed using the inherent and unique characteristics of direction of arrival (DOA) of the received signal. Under a simple hypothesis test, the DOA and rotational state features of the end device are used in designing the decision metric. The probability of false alarm and detection are analyzed based on the developed decision metric and their closed-form expressions are derived, based on which the optimal threshold can be obtained. Simulation results show that compared with the existing method in the literature, the proposed scheme outperforms significantly in terms of detection accuracy.
State Estimation with Static Displacement Compensation for Large-Scale Manipulators*

Tasks of large-scale manipulators on construction sites are usually planned for the tool center point in absolute coordinates. Automatic tool center point control requires an accurate position feedback either from measurements or from an estimator. Equipping mass-produced construction machines with high-precision and redundant sensors is expensive. Thus, a state estimator based on only one sensor for each joint is developed as an alternative in this work. Using a prediction step, a Kalman filter smooths the sensor measurements without creating a phase delay yielding estimated joint positions and velocities, from which the tool center point position is then estimated using forward kinematics. A static displacement model is derived based on the Euler-Bernoulli beam equation to account for not captured boom deflections caused by the flexible manipulator structure and the effect of gravity. This cheap, computationally light, and easy approach results in a position estimate for large-scale manipulators with an accuracy of a few centimeters throughout the whole task space and a maximum relative error of 2.53 % w.r. t. the manipulator outreach.
Fun with the R Grid Package

The increasing popularity of R is leading to an increase in its use in undergraduate courses at universities (R Development Core Team 2008). One of the strengths of R is the flexible graphics provided in its base package. However, students often run up against its limitations, or they find the amount of effort to create an interesting plot may be excessive. The grid package (Murrell 2005) has a wealth of graphical tools which are more accessible to such R users than many people may realize. The purpose of this paper is to highlight the main features of this package and to provide some examples to illustrate how students can have fun with this different form of plotting and to see that it can be used directly in the visualization of data.
Smartphone Based Tactile Feedback System Providing Navigation and Obstacle Avoidance to the Blind and Visually Impaired

People with vision impairments and other visual disorders require support to complete daily tasks like moving around and discovering new places. They may find it difficult to navigate through a new place and could be put in danger if they run into unforeseen barriers or could get lost easily. This paper discusses a solution to aid the blind and visually impaired navigate independently while avoiding obstacles in their path. The system has two operational modes, Outdoor Navigation and Indoor Navigation, and it alerts the user through vibration (tactile) and audio feedback. While the Indoor mode accompanies a user to a labelled site, the Outdoor mode leads them to a geographical destination. The solution was able to reduce the Clearance time by 27.35 percent and the Obstacle hit rate by 66.6 percent. The system is hassle-free, comfortable to use and affordable because it requires only the user's smartphone and a custom-made hardware waist belt.