A scheme to detect sand/dust weather applying meteorological radars

Sandy dust weather occur frequently in arid and semi-arid areas. It is important to actually detect the sandy dust grain concentration or the visibility of the sandy dust weather for weather forecasting. In this paper, based on numerical calculation of the effective detection distance of different radar detecting the sandy-dust weather with different strength, a scheme to detect sand/dust weather applying existed meteorological radar stations is proposed in this paper. The scheme can be efficient to detect sandy dust weather, for it makes a good supplement to the current deficiencies in detecting sandy dust weather and it’s a cost-saving detection way by using the existed meteorological radars. In addition, the effect of charges carried by sand/dust grains and the relative humidity on the effective detection distance of radar is also investigated, and it shows that these effects will not change the proposed scheme. It will be promising to detect the sandy dust weather in the way of disastrous weather precaution by using this scheme.

The IMPLEMENTATION OF SYSTEM DOOR ACCES USING FACE RECOGNITION AND FINGER PRINT FOR MUNITIONS WAREHOUSE WITH A BASE HAAR CASSCADE

Technology in the military world has been growing with the existence of technological innovations that are used to secure munitions warehouses. This is done to reduce the negligence of the munitions warehouse guard personnel if they lose the key from the munitions warehouse it can be monitored in real-time to find out who is around the munitions warehouse door. Thus, face recognition and fingerprint technology were created as a system for securing munitions warehouses, as well as the function is to be a CCTV. So, they can be monitored in real-time. This research method uses an experimental method to obtain quantitative data to prove the hypothesis data that use the Haar Cascade. The results of this study indicate that this security system has a very important role to limit access to and from the munitions warehouse and reduce the occurrence of theft or misuse of munitions. In the tests carried out, the minimum detection distance was 20 cm and the maximum distance that was able to detect was 110 cm. this is influenced by the focus of the camera and testing of the intensity of light in the morning, afternoon, evening, and night can be detected except detection at night without light and must use a fingerprint.

Method for Estimating Canopy Thickness Using Ultrasonic Sensor Technology

The accurate detection of canopy characteristics is the basis of precise variable spraying. Canopy characteristics such as canopy density, thickness and volume are needed to vary the pesticide application rate and adjust the spray flow rate and air supply volume. Canopy thickness is an important canopy dimension for the calculation of tree canopy volume in pesticide variable spraying. With regard to the phenomenon of ultrasonic waves with multiple reflections and the further analysis of echo signals, we found that there is a proportional relationship between the canopy thickness and echo interval time. In this paper, we propose a method to calculate canopy thickness using echo signals that come from ultrasonic sensors. To investigate the application of this method, we conducted a set of lab-based experiments with a simulated canopy. The results show that we can accurately estimate canopy thickness when the detection distance, canopy density, and canopy thickness range between 0.5and 1.5 m, 1.2 and 1.4, and 0.3and 0.6 m, respectively. The relative error between the estimated value and actual value of the simulated canopy thickness is no higher than 8.8%. To compare our lab results with trees in the field, we measured canopy thickness from three naturally occurring Osmanthus trees (Osmanthus fragrans Lour). The results showed that the mean relative errors of three Osmanthus trees are 19.2%, 19.4% and 18.8%, respectively. These results can be used to improve measurements for agricultural production that includes both orchards and facilities by providing a reference point for the precise application of variable spraying.

Virtual Reality Application on Road Markings’ Visibility Analysis

The most important function of road markings is to guide road users; therefore, their visibility, in relation to retroreflectivity (RL), plays an essential role in the markings’ performance at nighttime. Researchers have applied field experiments to determine the minimum acceptable levels of markings’ RL. However, field studies usually involve a large investment of time, personnel, space, and budget. This paper presents a preliminary study that adopted virtual reality (VR) techniques to establish an immersive driving environment for the RL analysis of yellow markings. The objective is to evaluate the minimum required marking visibility for road users, especially senior drivers, while driving at nighttime. The brightness feature created in the VR model does not simulate the optical behavior of markings’ glass beads, but it simulates the marking’s visibility in relation to detection distance at each test drive. This is an alternative method to correlate the field RL and the VR simulation model through the equivalent detection distances that were recorded by the observers who participated in both field and VR driving tests. The relationship between the field markings’ RL, 80 to 130 mcd/m2/lx, and simulated markings’ brightness was successfully established with its limitations of RL between 80 and 130 mcd/m2/lx. Three age clusters are involved in this study, namely 20–30, 40–50, and 60+ years old, with 30 people in each cluster. The simulation study found that the driver’s behavior in relation to reaction time is highly related to age cluster but not driving speed or RL. The average reaction times for the three age clusters are 0.438 s, 0.499 s, and 0.522 s, respectively. However, the perception reaction time 2.5 s was considered while calculating the required RL for markings. Results found that the minimum required yellow markings’ RL increases with driving speed, and it requires 80 mcd/m2/lx for 50 km/h and 130 mcd/m2/lx for 65 km/h to satisfy the 85 percentiles of the driving population in Taiwan.

Human Factors Assessment of Pavement Marking Retroreflectivity in Simulated Rain and Dry Conditions

Pavement marking retroreflectivity standards are typically developed with dry conditions in mind, however, driving at night during rainfall is seemingly one of the most challenging and stressful situations for a driver. Furthermore, existing research indicates continuous wet retroreflectivity is relatively weakly correlated with dry retroreflectivity and deteriorates differently over time, leading to the obvious conclusion that dry retroreflectivity standards alone are not enough to ensure that pavement markings meet the needs of drivers across the breadth of roadway conditions that may occur. Consequently, developing standards for minimum continuous wet retroreflectivity for new installations and for maintenance purposes represents an important area for research. This study aims to develop new installation and maintenance values for continuous wet retroreflectivity based on a multifaceted, closed-course study of detectability of pavement markings in simulated rain and dry conditions. A series of 20 pavement marking samples was evaluated in relation to detection distance and subjective rating. The results of the study indicated that pavement markings need to be maintained at a continuous wet retroreflectivity value of 50 mcd/m2/lux based on a participant pool that skewed older in age, but that likely represents something close to the 85th-percentile driver. Additional salient findings included observed wet retroreflectivity loss in the existing literature of approximately 7% per month, as well as the maximum preview time in simulated rain conditions being substantially lower than in dry conditions.

Out-of-sight or out-of-site? Forays and detection probabilities in single-season occupancy models

Occupancy models have become popular in wildlife survey analyses because they account for the frequent failure to detect individuals of targeted species. Those individuals sometimes move outside sampling sites, i.e. exhibit temporary emigration. In such cases, occupancy models may become difficult to interpret or even misleading either at the species or the individual level, because they confound presence at the site, availability for detection given presence, and actual detection by the observer. We quantified the probabilities of these three components with spruce grouse (Falcipennis canadensis) in southern Quebec, Canada. We conducted call-response surveys of 24 grouse monitored by radio-telemetry. We defined sites empirically as circular areas of 83 m radius centered on the observer, corresponding to the maximum detection distance obtained. Based on telemetry locations, grouse were present at the site during 42 % of the surveys. Six stationary grouse were present during surveys, but were never detected. Thus, only individuals that moved in the presence of the observer (89 %) were considered available for detection. Individuals available for detection were detected in 51 % of the cases. We simulated detection histories and built single-season occupancy models, based on the empirical relationship between detection probability and the distance measured between observers and grouse. When temporary emigration was ignored, site occupancy was ψ = 0.89, and the associated probability of detection was p = 0.23. When instances of temporary emigration were dropped, estimates were ψ = 0.88 and p = 0.41. Using only grouse available for detection, estimates were ψ = 0.87 and p = 0.42. Disentangling the components of detection probabilities had little impact on occupancy estimates, but showed a major effect of temporary emigration on estimated detection probabilities.

A New Challenge: Detection of Small-Scale Falling Rocks on Transportation Roads in Open-Pit Mines

In transportation at open-pit mines, rocks dropped as a mining truck is driven will wear out the tires of the vehicle, thus increasing the mining cost. In the case of autonomous vehicles, the vehicle must automatically detect rocks on the transportation roads during the driving process. This will be a new challenge: rough road, rocks of small size and irregular shape, long detection distance, etc. This paper presents a detection method based on light detection and ranging (lidar). It includes two stages: (1) using the modified cloth simulation method to filter out the ground points; (2) using the regional growth method based on grid division to cluster non-ground points. Experimental results show that the method can detect rocks with a size of 20–30 cm at a distance of 40 m in front of the vehicle, and it takes only 0.3 s on an ordinary personal computer (PC). This method is easy to understand, and it has fewer parameters to be adjusted. Therefore, it is a better method for detecting small, irregular obstacles on a low-speed, unstructured and rough road.

DECIPHERING THE CAPABILITIES OF LOOK-AHEAD METHODS IN LWD

The use of electromagnetic logging tools while drilling to investigate the space in front of the drill-bit and to predict the geologic strata and/or fluid property ahead has been one of the industry-focuses in recent years. The commercial look-ahead tools have two main designs based on the published tool configurations and data-channel definitions. The first type employs combinations of tilted coils to acquire multiple or all components of electromagnetic fields and presents logs defined in novel expressions as functions of these field components. The second kind uses combination of the voltage measurements from orthogonal coils (coils pointing along and perpendicular to tool-axis) and the propagation resistivity logs from coaxial coils of large coil-spacing. This work starts with reviewing the tool physics and the mathematic expressions defining the logs. The look-ahead capabilities are then investigated by considering the case where a borehole is perpendicular to the formation boundaries. It is found that the necessary information for forward detection applications is exclusively from the apparent resistivity logs, not the so-called geo-signals or any other logs. We further explore an ideal scenario, i.e., to detect the presence of a good conductor in front of a high resistivity formation to obtain the asymptotic behaviors for the two measurement types. The asymptotic expressions lay the foundation to understanding the intrinsic differences between the two methods. The insights derived from the theoretical work and the subsequent modeling results for a broad set of models define the response characteristics and the probing capabilities of the look-ahead techniques. It is realized that the look-ahead instrument design can be simplified, such as using only the familiar three-coil coaxial configuration with a suitable long coil-spacing and multiple carefully chosen operating frequencies to achieve a robust detection-distance coverage. It is worth to point out that the commercially available look-ahead services are mainly applicable to vertical and inclined wells so far. For look-ahead applications in horizontal wells, due to the contribution of the geologic and fluid boundaries lateral to the tool and the inhomogeneities ahead of the drill-bit, there is no reliable method yet to simultaneously solve the large number of unknown parameters associated other than some special situations. The look-ahead evaluation in the horizontal wells must be investigated further by understanding the characteristics of exponential attenuation and geometric spreading of the electromagnetic fields in a conductive formation and the inherent relatively poor resolution through combining with other detection physics and the introduction of proper geologic model constraints.

Training and usage of detection dogs to better understand bumble bee nesting habitat: Challenges and opportunities

Bumble bees are among the most imperiled pollinators. However, habitat use, especially nest site selection, remains relatively unknown. Methods to locate nests are invaluable to better understand habitat requirements and monitor wild populations. Building on prior study findings, we report constraints and possibilities observed while training detection dogs to locate bumble bee nests. Three conservation detection dogs were initially trained to three species of bumble bee nest material, first within glass jars concealed in a row of cinder blocks, then placed in the open or partially hidden for area searches. The next intended training step was to expose the dogs to natural nests located by community science volunteers. However, significant effort (> 250 hrs), yielded only two confirmed, natural nests suitable for dog training purposes. Although the dogs did not progress past the formative training stage valuable insight was gained. Maximum observed detection distance for bumble bee nest material during initial controlled training was 15 m, which decreased significantly (< 1 m) once training progressed to buried samples and natural nests. Three main considerations around future training and usage of detection dogs were identified. First, dogs might benefit from transitional training via exposures to known natural nests, regardless of species. However, it may be too difficult for people to find natural nests for this, and prior work demonstrated the ability of dogs to generalize and find natural nests after testing to artificially-buried nest material. Second, confirming a dog’s nest find, via resident bee presence, is nuanced. Third, future study design and objectives must harness strengths, and reflect limitations of detection dog surveys and search strategies, as extensively discussed in this paper. Prospective studies involving detection dogs for locating bumble bee nests would benefit from considering the drawbacks and opportunities discussed and can mitigate limitations through incorporating these considerations in their study design.

Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety

A smart helmet-based wearable personnel proximity warning system was developed to prevent collisions between equipment and pedestrians in mines. The smart helmet worn by pedestrians receives signals transmitted by Bluetooth beacons attached to heavy equipment, light vehicles, or dangerous zones, and provides visual LED warnings to the pedestrians and operators simultaneously. A performance test of the proposed system was conducted in an underground limestone mine. It was confirmed that as the transmission power of the Bluetooth beacon increased, the Bluetooth low energy (BLE) signal detection distance of the system also increased. The average BLE signal detection distance was at least 10 m, regardless of the facing angle between the smart helmet and Bluetooth beacon. The subjective workload for the smartphone-, smart glasses-, and smart helmet-based proximity warning system (PWS) was evaluated using the National Aeronautics and Space Administration task load index. All six workload parameters were the lowest when using the smart helmet-based PWS. The smart helmet-based PWS can provide visual proximity warning alerts to both the equipment operator and the pedestrian, and it can be expanded to provide worker health monitoring and hazard awareness functions by adding sensors to the Arduino board.