#smartenvironment

[labtronequipments]

UV Absorption Ozone Meter

Labtron UV absorption ozone meter offers large data storage capacity (16,000 samples) and uses PTFE inert material in the gas flow pipeline to reduce contamination. Operating between 0°C to 40°C, it records real-time data at a 1 L/min flow rate and features a built-in printer for easy data printing.

[kelurahanmanggaraiselatan]

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Smartcitizen, dalam rangka mendukung perbaikan kualitas udara dan mendorong penggunaan kendaraan berbasis listrik di ibukota, Pemprov @dkijakarta Jakarta berkolaborasi dengan PT @pln_id menggelar karnaval Jakarta Langit Biru 2019 pada Minggu, 27 Oktober 2019.⁣ ⁣ Karnaval ini diikuti oleh berbagai jenis kendaraan listrik yang ramah lingkungan, mulai dari skuter, sepeda, motor, becak, bajaj, mobil, taksi, dan bus, memeriahkan Jakarta Langit Biru 2019 yang mengambil rute pulang-pergi dari Bundaran Senayan menuju Bundaran Hotel Indonesia⁣ ⁣ Tak hanya itu saja, PT PLN juga melakukan sosialisasi 1.922 stasiun pengisian kendaraan listrik umum (SPKLU), sehingga akan semakin banyak masyarakat yang beralih ke kendaraan ramah lingkungan.⁣ ⁣ Ingin tahu bagaimana keseruan karnaval tersebut? Yuk simak video berikut!⁣ ⁣ #JakartaSmartCity #SmartEnvironment (di Bunderan Senayan) https://www.instagram.com/p/B4MWLS9hMeG/?igshid=1fqkrpiw87nab

[kelurahanmanggaraiselatan]

View this post on Instagram

Smartcitizen, dalam rangka mendukung perbaikan kualitas udara dan mendorong penggunaan kendaraan berbasis listrik di ibukota, Pemprov @dkijakarta Jakarta berkolaborasi dengan PT @pln_id menggelar karnaval Jakarta Langit Biru 2019 pada Minggu, 27 Oktober 2019.⁣ ⁣ Karnaval ini diikuti oleh berbagai jenis kendaraan listrik yang ramah lingkungan, mulai dari skuter, sepeda, motor, becak, bajaj, mobil, taksi, dan bus, memeriahkan Jakarta Langit Biru 2019 yang mengambil rute pulang-pergi dari Bundaran Senayan menuju Bundaran Hotel Indonesia⁣ ⁣ Tak hanya itu saja, PT PLN juga melakukan sosialisasi 1.922 stasiun pengisian kendaraan listrik umum (SPKLU), sehingga akan semakin banyak masyarakat yang beralih ke kendaraan ramah lingkungan.⁣ ⁣ Ingin tahu bagaimana keseruan karnaval tersebut? Yuk simak video berikut!⁣ ⁣ #JakartaSmartCity #SmartEnvironment (di Bunderan Senayan) https://www.instagram.com/p/B4MWLS9hMeG/?igshid=1ngcixckn1y26

[kelurahanmanggaraiselatan]

View this post on Instagram

Smartcitizen, dalam rangka mendukung perbaikan kualitas udara dan mendorong penggunaan kendaraan berbasis listrik di ibukota, Pemprov @dkijakarta Jakarta berkolaborasi dengan PT @pln_id menggelar karnaval Jakarta Langit Biru 2019 pada Minggu, 27 Oktober 2019.⁣ ⁣ Karnaval ini diikuti oleh berbagai jenis kendaraan listrik yang ramah lingkungan, mulai dari skuter, sepeda, motor, becak, bajaj, mobil, taksi, dan bus, memeriahkan Jakarta Langit Biru 2019 yang mengambil rute pulang-pergi dari Bundaran Senayan menuju Bundaran Hotel Indonesia⁣ ⁣ Tak hanya itu saja, PT PLN juga melakukan sosialisasi 1.922 stasiun pengisian kendaraan listrik umum (SPKLU), sehingga akan semakin banyak masyarakat yang beralih ke kendaraan ramah lingkungan.⁣ ⁣ Ingin tahu bagaimana keseruan karnaval tersebut? Yuk simak video berikut!⁣ ⁣ #JakartaSmartCity #SmartEnvironment (di Bunderan Senayan) https://www.instagram.com/p/B4MWLS9hMeG/?igshid=qwwa56m5ehpx

[btrcvblog-blog]

Improving Posture with Depth Camera

Using Microsoft Kinect which has a build in depth camera, researchers from the university of Hong Kong has developed a more accurate way of detecting posture. They use classify behavior, kinematics, colour and depth as reliability terms to produce more accurate detection. The application has potential uses in smart offices, laborious work environments and hospitals.

Researchers from the University of Hong Kong are developing a framework for human activity monitoring that uses depth cameras (Microsoft Kinect) to determine 3D posture. New concepts that are introduced in this paper are a set of reliability measurements use to produce increasingly more accurate joint detection. The reliability terms used include behavior, kinematics, colour and depth. 2D analysis techniques used in the past lacks sufficient information and motion capture techniques required extensive set up. This new framework produces more reliable results through machine learning algorithms that combining depth information, context of the activity, and available background knowledge of possible range of motion. The results are surprisingly accurate and can identify differences in posture that are imperceivable to the untrained human eye in seconds. Real-world applications of this technique could increase postural health in smart offices, reduce repetitive stress injuries for the workforce and aid in early detection of several musculoskeletal diseases. This framework can be applied to detecting mistakes in athletic form and provide valuable analysis of how injuries can occur. A cross discipline approach with the field of kinesiology and sports medicine could yield greater understanding of the human body.

Ho, Edmond S.L., et al. “Improving Posture Classification Accuracy for Depth Sensor-Based Human Activity Monitoring in Smart Environments.” Computer Vision and Image Understanding, vol. 148, July 2016, pp. 97–110,  doi:10.1016/j.cviu.2015.12.011.

[bodies-without-organs]

Concern in Ubiquity

Ulrik Ekman offers this perspective on what is unnerving about computational systems, like targeted advertising, AI assistants, or data-driven crowd management, that use covertly gathered information to tailor their behaviour to individual circumstances:

A supposedly ‘natural’ setting turns out to be nothing if not a highly artificial context or an information-intensive environment, and it appears attentively oriented towards us rather than being neutral or perfectly non-caring. [1]

The peculiarity common to these encounters stems from the perception of agency where none is expected. Environments, whether natural or artificial, are not typically the sort of things we take to care about anything, let alone about us. While some, such as buildings, may be designed by humans with human needs in mind, it is not yet natural to expect such environments to respond dynamically to the needs of particular people in particular moments.

Two facets can be discerned in the ‘attentiveness’ present in such systems: they both (i) gather data, and (ii) do things in response to this data. This alone seems insufficient to distinguish these systems from those which respond to changes in ambient conditions without inviting attributions of agency. Thermostats provide one example, their behaviour analysable in terms of cause and effect, requiring no appeal to agential concepts like care, concern, or attentiveness.

What does seem critical in furnishing the systems Ekman describes with a sense of agency is the presence---real or imagined---of intention in their behaviour. They do not gather their data indiscriminately. Intention constrains and structures data-gathering processes, enacting discriminatory practices that delimit categories of revelant and discardable information. Intention is what allows us to describe the causal path from environmental event to sensor to database as a kind of sensing; what allows us to describe the response of the system as a kind of acting.

If it is the feeling that such systems sense us and act towards us that imparts them with their often unsettling shades of agency, and an event being a sensing or an acting is dependent on its being intended, we might ask to what extent is this agency parasitic on the agency of the humans who built these systems in line with their own purposes and intentions? The primary intention of targeted advertising may be to display to a person adverts relevant to their interests, but if this intention only exists in virtue of the human intention to profit from the targeting, can we not disregard the ‘agency’ of the system itself as being of a derivative kind, a metaphor for what is ultimately no more than a patterning of causes and effects determined by external human agents?

But perhaps denying agency to these systems on the basis of such strict criteria is unkind. Isn’t it too much to ask of a sensing, acting artefact that its behaviour be wholly independent of the intentions of its artisan? What seems more reasonable (and more permissive) in assessing its claim to agency is that it senses and acts with autonomy, that is that it acts without explicit human direction. And this is a feature these systems certainly exhibit.

A final consideration: in addition to their creators, what hand do the humans who experience and interact with them have in constituting the perceived agency of these systems? After all, it is questionable whether it would even occur to us to describe them as caring if it was not us they sensed, us they acted for?

But in this case also it seems possible to generalise. A system that dispensed food to animals based on the detection of behaviours associated with hungriness would attract the same attributions of agency, for all the same reasons. And if, say, there was a machine that performed optimally at a certain temperature, and there was a separate system which monitored the conditions in the room housing it, adjusting the temperature to meet optimal conditions, would not all the same factors be in play here too? Would we not want to say of this nonhuman system that despite its lack of any interfaces facilitating human interaction, there is nevertheless an agency present in its attentive care to a nonhuman other?

Notes:

Ulrik Ekman, Complex Ubiquity-Effects