Illumination Sensor Research Articles

Defect Detection for a Vertical Shaft Surface Based on Multimodal Sensors

Hydroelectricity is a major source of renewable electricity originating from a turbine driven by dammed large-volume water via a penstock or a drop shaft. Shafts suffer from risks of collapse due to the pressure from exterior structures and the erosion from inner water flow. Vertical shafts are an important part of hydroelectric power generation systems, and detecting defects in shafts guarantees the stable operation of hydropower stations. However, shaft defect detection is a great challenge due to the poor conditions, large drop in height, limited entrance size, lack of light and damp air, where suitable technology and equipment are not available. Aiming at defect detection for vertical shafts, we have developed a defect-detecting system based on unmanned airships, integrated panoramic CCD cameras, 3D laser scanners, inertial measurement units, barometric altimeters, illumination sensors, and control modules. Shaft defect detection methods (SDDMs) are proposed by fusing the multi-modal image features to extract typical defects on concrete surfaces. Compared with machine learning methods, the proposed method achieves the highest overall accuracy of 90.90% for defect detection. Our system was validated by experiments in the shafts of the Nuozhadu hydropower station to be functional for defect detection, which demonstrates its capability of reducing the risk of collapse and improving safety.

Research on Modelling Method of Natural Illuminance Based on RBFNN

Aiming at the problem that the indoor natural illuminance is difficult to calculate accurately due to many complex factors in the environment. This paper proposes a method to establish the natural illuminance model by using radial basis function neural network (RBFNN). The RBFNN was trained by collecting indoor natural light data to obtain the benchmark model for calculating the natural illuminance distribution. The illuminance sensor arranged indoors was used to measure the real-time natural illuminance, and the output of the benchmark model was modified accordingly, so as to obtained a model that could calculate the real-time natural illuminance at any point in the room. The experimental results show that the maximum normalized error between the calculated and measured illuminance values of the model in this paper is no more than 15%, and the error of most test data is less than 8%, which solves the problem of accurate calculation of indoor natural illuminance, so as to provide support for indoor lighting control system.

Research on Monitoring Algorithm and Induction Trigger System of New Spectrophotometer

In the traditional laboratory engineering bacteria culture work, it is necessary to frequently sample the bacterial solution and judge the growth period by photometric method. These operations are accompanied by pollution risk and the change of culture environment. In this paper, a design scheme of illumination monitoring algorithm of new spectrophotometer based on single chip microcomputer is proposed, The absorbance value is calculated by single chip microcomputer and the threshold is preset to trigger the induction action, so that the whole process from the beginning of culture to the triggering induction of engineering bacteria can be intelligent, automatic and non-contact. The new spectrophotometer takes STM32F103 series processor as the main control chip, and the system is mainly composed of main control board and induced action trigger device. The main control board mainly includes optical density calculation, 12 LED light control, 12 action machine control and human-computer interaction module; The induced action trigger device is composed of motor, bh1750 module, monochrome light source and other parts, which can realize illuminance detection, absorbance calculation, preset absorbance value and trigger the action machine. The system automatically adjusts the working mode of the sensor through the matrix keyboard input, displays the image through a liquid crystal display, uses the bh1750 illuminance sensor module to convert the input illuminance signal into electrical signal for wired output, processes the sensor data through MCU, performs logical operation with the preset value and performs corresponding action. The main control part of the system is small, and the detection method is simpler and faster than the traditional detection. The logic operation part is cleverly designed, the trigger time of the induced action is flexible and adjustable, and there is an adjustable timer after the induced action is triggered.

Indoor Device-free Localization Using Received Signal Strength Indicator and Illuminance Sensor for Random-forest-based Fingerprint Technique

Indoor Device-free Localization Using Received Signal Strength Indicator and Illuminance Sensor for Random-forest-based Fingerprint Technique

Study on the effect of awakening daylight in dormitories on morning alertness, mood, fatigue and sleep quality of college students

Awakening lighting has an important influence on circadian rhythm, alertness, mood and other physiological and psychological indexes. In a real situation with no electric awakening light, psychological suggestion or interference, the influencing mechanism of awakening daylight on human physiology and psychology is explored from the awakening data of dynamic daylight. This data can further guide the application of electric awakening lighting. The effects of daylight exposure at eye level on sleep quality, morning alertness, mood and fatigue of college students in dormitories in Beijing were studied. The subjects were 8 male and 8 female students from 4 dormitories in two adjacent apartments, and field experiments were conducted for 36 days in winter and 30 days in summer. The real-time illuminance of daylight before awakening was recorded by a wireless illuminance sensor, and the amount of light exposure and the maximum illuminance before awakening were calculated. The evaluation data of physiological and psychological indexes such as sleep quality, morning alertness, mood and fatigue were obtained by using sleep monitoring equipment—Jawbone UP3, PSQI, KSS, positive and negative semantic scales. The results showed that the lighting exposure accumulation before awakening in summer were significantly correlated with morning alertness, mood and deep sleep duration and there was a significant correlation between the Emax and morning alertness in summer. In addition, there were significant differences in deep sleep duration and morning alertness between different groups of lighting exposure accumulation in summer. There was no significant correlation between the lighting exposure accumulation or max value of illuminance and the physiological and psychological indexes in winter.

Effects of Window Films in Thermo-Solar Properties of Office Buildings in Hot-Arid Climates

The electricity consumption in residential/office buildings corresponded to 45% of the total annual electricity demand in hot-arid climates. This accounted for 27.2 TWh of electricity consumption with 14.2 MWh/capita/year in Kuwait. In this research, four offices in an educational building were equipped with a meteorological data logging system using temperature, humidity, and illuminance sensors. All four offices had double-glazed windows. Moreover, two offices were equipped with two types of commercially available window films. Two million data were stored in iCloud using Wi-Fi and an Internet of Things (IoT) system for the 3 months of June, July, and August 2019. Here, histograms and the kernel density estimation (KDE) of temperature/humidity were analyzed and compared for the two offices with/without 3M Neutral 20 window films. Two floors of the same building consisting of 31 offices were also modeled and simulated to study energy saving and CO2 footprint reduction using various window films. The results of simulations for the month of July 2019 using SOL 101 and SOL 102 window films, respectively, showed that about 250 kg and 255 kg of production of CO2 could be reduced and energy saving counted for 416 and 422 kWh. Measurements from offices with 3M Neutral 20% and 3M Neutral 70% window films for the month of July 2019 indicated that the carbon footprint could be reduced by about 82 kg and 0.43 kg and energy saving counted for 147.11 and 0.71 kWh, respectively. It was observed that an annual energy saving and CO2 footprint reduction of 2.76% could be achieved using window films in a hot-arid climate.

Design and study of multifunctional illumination sensors based on spectral analysis

In this article, we present a design method for multifunctional illumination sensors, which can simultaneously detect blue light hazard, circadian rhythm, as well as scotopic and photopic vision based on spectral analysis. We showed that illumination sensors for blue light hazard, circadian rhythm, scotopic vision, and photopic vision can be fabricated based on attaching the Si photodiode S5981 to the bandpass filters DTB435, DTB470, DTB500, and DTB545 with a thickness of 4 mm. The deviations of the central wavelengths of their response curves from those of the four response functions of the human eye are 3, 11, –3, and –8 nm; the corresponding deviations of the full widths at half maximum are 16, 5, 1, and –7 nm, and the corresponding relative deviations of the integrals over the visible wavelength range of 380–780 nm are 32.0%, 15.2%, 4.5%, and 1.9%. The average absolute and relative errors of the values measured with our self-made photopic vision illumination sensor in ten independent measurements are 7.82 lx and 1.77%, respectively. Six kinds of light sources were measured with the circadian rhythm illumination sensor, and all relative errors are less than 5%, and the average value is 2.63%. The experimental results showed that this design method is feasible, and the research can provide a reference for enriching optical bioluminescence sensors.

A Redundant Sensing Elimination Technique for Improving Energy Efficiency of IoT Sensor Networks

Various sensor nodes used in IoT network systems composed of electronic devices perform various control tasks using the results measured by the sensors. At this point, if these sensing tasks are smartly assigned and performed, the energy consuming efficiency of the entire networked system can be improved. That is, since each sensor node constituting the IoT network system performs redundant sensing and control devices, it is possible to improve the network’s energy efficiency by appropriately assigning and removing such redundant sensing tasks. In particular, the indoor illuminance sensor is installed in various devices such as automatic light switch systems, smart phones, and tablets. It is desirable that only one sensor node among them participates in the actual sensing task. In a smart home, redundant sensors and controllers are used repeatedly in refrigerators, heaters, TVs, artificial intelligence speakers, ventilation facilities, and smartphones. These redundant sensing and control signal generation lowers energy consumption’s efficiency. This study proposes a technique that can properly eliminate unnecessary and redundant sensing tasks that occur when such on the flown IoT network system is built. As a result, it has been shown that the efficiency of energy consumption can be improved in the entire IoT sensor network system.

릴리패드와 조도센서를 활용한 교통사고대비 인클루시브 안전조끼 디자인 개발 연구

Preventing accidents during various traffic accidents and night activities requires an inclusive LED safety vest design with the concept of safety design and inclusive design. In this study, we intend to develop an inclusive LED safety vest using a wearable computer, Arduino LilyPad, and a light sensor. In addition, when developing a fashion design with a wearable computer function, the design principles and characteristics to be considered based on the concept of safety design and inclusive design were identified and a guideline for producing a wearable computer design was proposed. By applying the inclusive LED safety vest manufacturing process and guidelines derived from the theoretical background, a wearable computer hardware device called LilyPad and an illumination (light) sensor that detects the driver

Design and Implementation of Crop Automatic Diagnosis and Treatment System Based on Internet of Things

This paper designs a smart agriculture Internet of Things system which combines crop disease detection with automatic diagnosis and treatment, and provides support for subsequent research on crop disease detection and control. The smart agricultural Internet of Things system uses TCS3200 colour sensor to identify leaf disease characteristics; colour sensor sends the data back; This system compares it with the recorded plant identification database, and carries out small-scale sprinkler irrigation diagnosis and treatment through valve irrigation system. The Internet of Things system consisting of STM32L431 single chip microcomputer, MG811 CO2sensor, BH1750 illumination sensor and DHT11 temperature and humidity sensor monitors environmental data, which is uploaded to the cloud by BC35 module through NB-IoT protocol and can be viewed by users on the display screen. Users can adjust the exhaust system, lighting system and humidification system in the system through the cloud or mobile phone to improve the environment and alleviate crop diseases. The experimental results show that the intelligent agricultural Internet of Things system designed in this paper has achieved the initial goal, and can roughly judge the disease types of plants by leaf colour, which has great commercial application value.

Climate change and thermo-solar patterns of office buildings with/without window films in extreme hot-arid climate of Kuwait

This research attempts to develop a systematic method to address climate changes by studying temperature-humidity patterns as the two major meteorological parameters and to moderate these effects using a commercially available window film on interior windows of an office building with double-glazing. A three-floor educational building at Australian College of Kuwait (ACK) was equipped with temperature, humidity and illuminance sensors for two similar size offices, one with 3 M Neutral 20 window films. Total readings of 50,000 entries from each sensor was recorded every 3 min by a home-designed microprocessor-based logging system for three months of June, July, and August 2019. In parallel, the ACK building was simulated in EnergyPlus and DesignBuilder software and calibrated with the experimental measurements. Histograms and a probability density function (PDF) of temperature and humidity are built by a representative rational function as a model of main climate parameters variations. By examining min-mean-max values of data, it is observed that the office with window films has increased indoor humidity and, in most instances, reduced temperature by 2–5 °C compared to double-glazed bare windows which can significantly reduce cooling loads in extreme hot-arid climates. Simulation results are presented on the energy saved and the reduced CO2 footprints by applying the solar window films in the ACK building.

Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User's Location.

Ultraviolet rays are closely related with human health and, recently, optimum exposure to the UV rays has been recommended, with growing importance being placed on correct UV information. However, many countries provide UV information services at a local level, which makes it impossible for individuals to acquire user-based, accurate UV information unless individuals operate UV measurement devices with expertise on the relevant field for interpretation of the measurement results. There is a limit in measuring ultraviolet rays’ information by the users at their respective locations. Research about how to utilize mobile devices such as smartphones to overcome such limitation is also lacking. This paper proposes a mobile deep learning system that calculates UVI based on the illuminance values at the user’s location obtained with mobile devices’ help. The proposed method analyzed the correlation between illuminance and UVI based on the natural light DB collected through the actual measurements, and the deep learning model’s data set was extracted. After the selection of the input variables to calculate the correct UVI, the deep learning model based on the TensorFlow set with the optimum number of layers and number of nodes was designed and implemented, and learning was executed via the data set. After the data set was converted to the mobile deep learning model to operate under the mobile environment, the converted data were loaded on the mobile device. The proposed method enabled providing UV information at the user’s location through a mobile device on which the illuminance sensors were loaded even in the environment without UVI measuring equipment. The comparison of the experiment results with the reference device (spectrometer) proved that the proposed method could provide UV information with an accuracy of 90–95% in the summers, as well as in winters.

Implementation and Evaluation of Novel Architecture Using Optical Wireless for WLAN Control Plane

We propose a novel wireless control system architecture that divides the wireless service area into smaller optical cells and centrally controls the user equipment (UE) connections under each optical cell. The proposal transmits via IoT smart lighting an optical identifier (ID) specifying connection information to the UE with illuminance sensor. The received optical ID indicates the optimum connection destination. Two solutions are proposed to overcome the hardware restrictions faced by optical ID transmission/reception. Oversampled edge-excluded receiving scheme (OE) reduces the error rate; the signal is oversampled and each window subjected to majority decision. Optical ID reception with lower error rate than conventional approach is realized with a minimum received illuminance of just 8.5 lx (background illuminance 226.67 lx) and a modulation factor of 7 %. Fast optical ID authentication (FA) reduces the authentication cycle preceding optical ID reception by performing correlation calculation between the cyclic matrix and an optical ID list. FA shortens the authentication cycle by 62.5 % (1200 ms to 450 ms). Furthermore, an optical cell control algorithm (ScanLine-based/MinDist-based) is proposed to offset the deterioration in network quality created by non-uniform UE distribution. ScanLine-based algorithm controls optical cells in scanning line manner, while MinDist-based favors optical cells with the minimum distance from the access point (AP). It is confirmed that with an optical cell radius of 5 m or less, the capacity difference per user is reduced compared to the existing RSSI-based alternative while reducing the deterioration of total capacity in extremely biased user distribution scenarios.

An automated louver with innovative parametrically-angled reflective slats: Prototyping and validation via using parametric control in Grasshopper along with Arduino board

Energy-saving potential in lighting is significant in office buildings due to their primary occupancy during the daytime. This potential can be realised with an advanced daylighting system being particularly able to deliver more uniform and steadier illumination. This paper presents prototyping and experimental validation of an automated louver with parametrically-angled reflective slats, in order to reflect sunlight to evenly distributed target positions on a ceiling. The illuminated ceiling acts as a source of diffuse light to the room. The physical rotation of the automated louver is controlled parametrically using Grasshopper software together with Arduino board, motors for the motion and illuminance sensors. Validation is an essential stage to ensure the performance of such systems. Two prototypes of the proposed system with different scales are presented, 1:20 and 1:1 scaled mock-ups. The monitoring results verify the principle and aim of the proposed design, i.e., more uniform and distributed daylight illumination, automation control method besides its practicality.

Simulating energy savings potential with high-resolution daylight and occupancy sensing in open-plan offices

Office lighting installations often inefficiently expend energy illuminating space that is unoccupied and/or already receiving adequate daylight. Illuminance and occupancy sensors can enable substantial energy savings by deploying electric lighting only in proximity to occupants, and at levels required to supplement available daylight. Savings vary based on sensor granularity, daylight availability, façade geometry and occupancy. In the current study, an open-plan office is simulated for 1 year to estimate energy savings relative to a conventional configuration. Available daylight is modelled using DAYSIM and occupancy is stochastically modelled in accordance with a standard schedule. Forty-eight combinations of simulation parameters are compared. Savings exceed 60% with high-resolution control. Greater savings are observed where daylight is available and occupancy is low. Estimated savings decrease to less than 35% with coarser lighting zones. The proposed workflow can assess the energy savings of occupancy- and/or daylight-based controls for open-plan offices of any common configuration.

A Cost-Effective Illuminance Sensor for DaylightHarvesting Lighting Control Systems

A cost-effective two-wire industrial standard illuminance sensor (4–20) mA has been proposed. It can be used for daylight harvesting control of indoor illuminance and other applications. The basic sensor used is a cadmium sulphide (CdS) light dependent resistor, whose relative spectral characteristic almost corresponds to the human eye. The method of sensor calibration has been presented and static and dynamic performance characteristics of the sensor have been experimentally determined.

Design and Real-Time Implementation of Cloud based Indoor Illumination Monitoring System

This study presents an IoT-based illumination monitoring system, which enables the users to measure lux values at multipositional indoor space using mobile sensors and store them in a cloud server network. The sensing unit is designed by the integration of an illumination sensor and IR transceiver for position detection with embedded microcontroller having on-chip Wi-Fi device placed over RF-operated car. The car when moves through a selected indoor space gathers positional lux values at certain time interval and stores them on the cloud server through a wireless transceiver system. The stored values are used to generate a lux map, which is compared with the benchmark, and the result of comparison is considered to be intelligent information for modification of the existing illumination system.

Energy savings and daylighting evaluation of dynamic venetian blinds and lighting through full-scale experimental testing

Commercial buildings consume around 20% of the total energy utilized in the U.S. The use of shading devices in buildings has the potential to reduce building energy consumption while also providing a more desirable indoor environment for building occupants. Similarly, lighting device controls help reduce lighting energy consumption as well as internal loads. However, there is lack of full-scale experimental testing of integrated dynamic shading and lighting controls. This study presents full-scale experimental testing data to quantify these energy and daylighting impacts. Testing was performed utilizing two different control strategies, in three different orientations under three different types of sky conditions. The shading device was also used in conjunction with two different types of glazing. An average of 25.4% cooling energy savings and 48.5% lighting energy savings was achieved. Visual discomfort evaluated using Simplified Daylight Glare Probability was prevented more than 85% of time during occupied hours. Improvement in daylight levels evaluated using Useful Daylight Illuminance was also achieved. Finally, analysis of the relationship between the illuminance level measured at the work plane and a ceiling illuminance sensor is performed to translate the real-world applicability of shading device controls.

A dimensionality reduction method to select the most representative daylight illuminance distributions

ABSTRACTOne challenge when evaluating daylight distribution is dealing with the large amount of temporal and spatial data, visualizations and variability in illuminances that are assessed in buildings. Using a dimensionality reduction method based on principal component analysis, we identified the most representative annual daylight distributions. We modelled a rectangular room containing an analysis grid of 3200 illuminance sensor points and simulated 3285 different temporal daylight conditions using an annual occupancy schedule ranging from 08:00 to 17:00 with one-hour sampling intervals in two locations: Singapore and Oakland, California. Our approach explained 98% of the illuminance variability with three daylight distributions in Singapore, and 92% using six in Oakland, California. Our dimensionality reduction strategy was also generalized using a complex building geometry showing the utility of the method. We think this approach can be used to provide a more efficient and reliable method to analyse daylight performance in building practice.

Development and Testing of Automatic Measurement Tools for Electrical Characteristics of Elements for Design Learning of Circuits with Illuminance Sensor

Development and Testing of Automatic Measurement Tools for Electrical Characteristics of Elements for Design Learning of Circuits with Illuminance Sensor